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Dr. Ali Sadollah
University of Science and Culture

Basic Info

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Research Keywords & Expertise

0 genetic algorithm
0 MetaHeuristic Algorigthm
0 optimization algorithm
0 Engineering Optimization
0 Articial intelligence

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Water Cycle Algorithm
genetic algorithm
optimization algorithm
Engineering Optimization
Neural Network Algorithm

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Short Biography

Ali Sadollah received his BS and MS degrees in Mechanical Engineering from Azad University, Iran in 2007 and University of Semnan, in 2010, respectively. He obtained his PhD at University of Malaya, Kuala Lumpur in 2013. He served as a research fellow for 2 years at Korea University and one year at NTU in Singapore. He has this honor to serve as a postdoc research fellow for one and half year at Sharif University of Technology, Tehran, Iran. Currently, he is assistant professor at University of Science and Culture, Tehran, Iran. Research interests: Soft Computing Applications in Engineering, Optimization and Metaheuristics, and so forth.

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Journal article
Published: 08 January 2021 in Applied Soft Computing
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This paper represents a hybrid Firefly and Self-Regulating Particle Swarm Optimization (FSRPSO) algorithm to solve optimal Combined Heat and Power Economic Dispatch (CHPED) problem. Valve point effect on fuel cost function of pure generation units, electrical power losses in transmission systems and feasible operating zones are taken into account in the CHPED problem. The CHPED refers to minimize total costs of fuel for electricity and heat generation supply to load demand. The proposed FSRPSO attempts to determine the start of the local search process properly by checking the previous global best. Thus, the FSRPSO is able to exploit strong points of both Firefly Algorithm (FA) and SRPSO mechanisms in order to balance between exploration and exploitation phases. Besides, for the sake of validation the proposed hybrid method, the FSRPSO is examined on 21 well-known benchmarks, and also a real engineering case i.e., two power systems for evaluating its performance compared with the SRPSO, FA, PSO, and other state-of-the-art algorithms. The obtained optimization results show that the proposed FSRPSO provides fast, mature and reliable optimum solutions and outperform other compared algorithms in diverse categories of benchmarks along with the studied CHPED problem.

ACS Style

Mohammad Nasir; Ali Sadollah; Ibrahim Berkan Aydilek; Afshin Lashkar Ara; Seyed Ali Nabavi-Niaki. A combination of FA and SRPSO algorithm for Combined Heat and Power Economic Dispatch. Applied Soft Computing 2021, 102, 107088 .

AMA Style

Mohammad Nasir, Ali Sadollah, Ibrahim Berkan Aydilek, Afshin Lashkar Ara, Seyed Ali Nabavi-Niaki. A combination of FA and SRPSO algorithm for Combined Heat and Power Economic Dispatch. Applied Soft Computing. 2021; 102 ():107088.

Chicago/Turabian Style

Mohammad Nasir; Ali Sadollah; Ibrahim Berkan Aydilek; Afshin Lashkar Ara; Seyed Ali Nabavi-Niaki. 2021. "A combination of FA and SRPSO algorithm for Combined Heat and Power Economic Dispatch." Applied Soft Computing 102, no. : 107088.

Journal article
Published: 18 November 2020 in Applied Sciences
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This study proposes a novel detection model for the detection of cyber-attacks using remote sensing data on water distribution systems (i.e., pipe flow sensor, nodal pressure sensor, tank water level sensor, and programmable logic controllers) by machine learning approaches. The most commonly used and well-known machine learning algorithms (i.e., k-nearest neighbor, support vector machine, artificial neural network, and extreme learning machine) were compared to determine the one with the best detection performance. After identifying the best algorithm, several improved versions of the algorithm are compared and analyzed according to their characteristics. Their quantitative performances and abilities to correctly classify the state of the urban water system under cyber-attack were measured using various performance indices. Among the algorithms tested, the extreme learning machine (ELM) was found to exhibit the best performance. Moreover, this study not only has identified excellent algorithm among the compared algorithms but also has considered an improved version of the outstanding algorithm. Furthermore, the comparison was performed using various representative performance indices to quantitatively measure the prediction accuracy and select the most appropriate model. Therefore, this study provides a new perspective on the characteristics of various versions of machine learning algorithms and their application to different problems, and this study may be referenced as a case study for future cyber-attack detection fields.

ACS Style

Young Hwan Choi; Ali Sadollah; Joong Hoon Kim. Improvement of Cyber-Attack Detection Accuracy from Urban Water Systems Using Extreme Learning Machine. Applied Sciences 2020, 10, 8179 .

AMA Style

Young Hwan Choi, Ali Sadollah, Joong Hoon Kim. Improvement of Cyber-Attack Detection Accuracy from Urban Water Systems Using Extreme Learning Machine. Applied Sciences. 2020; 10 (22):8179.

Chicago/Turabian Style

Young Hwan Choi; Ali Sadollah; Joong Hoon Kim. 2020. "Improvement of Cyber-Attack Detection Accuracy from Urban Water Systems Using Extreme Learning Machine." Applied Sciences 10, no. 22: 8179.

Conference paper
Published: 27 October 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this paper, Neural Network Algorithm is employed for simultaneous placing and sizing Distributed Generators and Shunt Capacitors Banks in distribution network to minimize active power loss and improve the voltage profile. The NNA is a novel developed optimizer based on the concept of artificial neural networks which benefits from its unique structure and search operators for solving complex optimization problems. The difficulty of tuning the initial parameters and trapping in local optima is eliminated in the proposed optimizer. The capability and effectiveness of the proposed algorithm are evaluated on IEEE 69-bus distribution system with considering nine cases and the results are compared with previous published methods. Simulation outcomes of the recommended algorithm are assessed and compared with those attained by Genetic Algorithms, Grey Wolf Optimizer, and Water Cycle Algorithm. The analysis of these results is conclusive in regard to the superiority of the proposed algorithm.

ACS Style

Mohammad Nasir; Ali Sadollah; Eneko Osaba; Javier Del Ser. A Novel Metaheuristic Approach for Loss Reduction and Voltage Profile Improvement in Power Distribution Networks Based on Simultaneous Placement and Sizing of Distributed Generators and Shunt Capacitor Banks. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 64 -76.

AMA Style

Mohammad Nasir, Ali Sadollah, Eneko Osaba, Javier Del Ser. A Novel Metaheuristic Approach for Loss Reduction and Voltage Profile Improvement in Power Distribution Networks Based on Simultaneous Placement and Sizing of Distributed Generators and Shunt Capacitor Banks. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():64-76.

Chicago/Turabian Style

Mohammad Nasir; Ali Sadollah; Eneko Osaba; Javier Del Ser. 2020. "A Novel Metaheuristic Approach for Loss Reduction and Voltage Profile Improvement in Power Distribution Networks Based on Simultaneous Placement and Sizing of Distributed Generators and Shunt Capacitor Banks." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 64-76.

Journal article
Published: 20 October 2020 in Trends in Computer Science and Information Technology
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ACS Style

Sadollah Ali. How do artificial neural networks lead to developing an optimization method? Trends in Computer Science and Information Technology 2020, 5, 067 -069.

AMA Style

Sadollah Ali. How do artificial neural networks lead to developing an optimization method? Trends in Computer Science and Information Technology. 2020; 5 (1):067-069.

Chicago/Turabian Style

Sadollah Ali. 2020. "How do artificial neural networks lead to developing an optimization method?" Trends in Computer Science and Information Technology 5, no. 1: 067-069.

Journal article
Published: 23 September 2020 in International Journal of Sustainable Transportation
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ACS Style

Mohammad Hadi Almasi; Yoonseok Oh; Ali Sadollah; Young-Ji Byon; Seungmo Kang. Urban transit network optimization under variable demand with single and multi-objective approaches using metaheuristics: The case of Daejeon, Korea. International Journal of Sustainable Transportation 2020, 15, 386 -406.

AMA Style

Mohammad Hadi Almasi, Yoonseok Oh, Ali Sadollah, Young-Ji Byon, Seungmo Kang. Urban transit network optimization under variable demand with single and multi-objective approaches using metaheuristics: The case of Daejeon, Korea. International Journal of Sustainable Transportation. 2020; 15 (5):386-406.

Chicago/Turabian Style

Mohammad Hadi Almasi; Yoonseok Oh; Ali Sadollah; Young-Ji Byon; Seungmo Kang. 2020. "Urban transit network optimization under variable demand with single and multi-objective approaches using metaheuristics: The case of Daejeon, Korea." International Journal of Sustainable Transportation 15, no. 5: 386-406.

Articles
Published: 27 August 2020 in Engineering Optimization
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In this article, a model of a T-shaped fin, consisting of a set of ordinary differential equations (ODEs), is considered. The purpose of this article is to numerically solve ODE systems of a T-shaped fin (there is no reported exact and analytical solution) using an alternative approach. Utilizing a base approximation function, some mathematical principles and metaheuristics, an approximate solution very close to the existing numerical solution was found. The weighted residual function is used as an objective function along with its constraints, such as boundary and initial values. For the sake of comparison, a generational distance metric is used to evaluate the obtained results compared with the existing results in the literature. The approximate solution found by the applied approach demonstrates its efficiency and performance compared with the existing numerical approach.

ACS Style

Ali Sadollah; Kaizhou Gao; Joong Hoon Kim. Memetic computing for imprecise solution of T-shaped heat transfer fins. Engineering Optimization 2020, 53, 1504 -1522.

AMA Style

Ali Sadollah, Kaizhou Gao, Joong Hoon Kim. Memetic computing for imprecise solution of T-shaped heat transfer fins. Engineering Optimization. 2020; 53 (9):1504-1522.

Chicago/Turabian Style

Ali Sadollah; Kaizhou Gao; Joong Hoon Kim. 2020. "Memetic computing for imprecise solution of T-shaped heat transfer fins." Engineering Optimization 53, no. 9: 1504-1522.

Review
Published: 19 June 2020 in Neural Computing and Applications
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In recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms.

ACS Style

Mohammad Nasir; Ali Sadollah; Young Hwan Choi; Joong Hoon Kim. A comprehensive review on water cycle algorithm and its applications. Neural Computing and Applications 2020, 32, 17433 -17488.

AMA Style

Mohammad Nasir, Ali Sadollah, Young Hwan Choi, Joong Hoon Kim. A comprehensive review on water cycle algorithm and its applications. Neural Computing and Applications. 2020; 32 (23):17433-17488.

Chicago/Turabian Style

Mohammad Nasir; Ali Sadollah; Young Hwan Choi; Joong Hoon Kim. 2020. "A comprehensive review on water cycle algorithm and its applications." Neural Computing and Applications 32, no. 23: 17433-17488.

Review
Published: 08 June 2020 in Applied Sciences
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Harmony Search (HS) is a music-inspired optimization algorithm for solving complex optimization problems that imitate the musical improvisational process. This paper reviews the potential of applying the HS algorithm in three countries, China, South Korea, and Japan. The applications represent several disciplines in fields of study such as computer science, mathematics, electrical/electronic, mechanical, chemical, civil, and industrial engineering. We anticipate an increasing number of HS applications from these countries in near future.

ACS Style

Mohammad Nasir; Ali Sadollah; Jin Hee Yoon; Zong Woo Geem. Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea. Applied Sciences 2020, 10, 3970 .

AMA Style

Mohammad Nasir, Ali Sadollah, Jin Hee Yoon, Zong Woo Geem. Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea. Applied Sciences. 2020; 10 (11):3970.

Chicago/Turabian Style

Mohammad Nasir; Ali Sadollah; Jin Hee Yoon; Zong Woo Geem. 2020. "Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea." Applied Sciences 10, no. 11: 3970.

Review
Published: 06 March 2020 in Sustainability
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In recent years, both sustainability and optimization concepts have become inseparable developing topics with diverse concepts, elements, and aspects. The principal goal of optimization is to improve the overall sustainability including the environmental sustainability, social sustainability, economic sustainability, and energy resources sustainability through satisfying the objective functions. Therefore, applying optimization algorithms and methods to achieve the sustainable development have significant importance. This paper represents a considerable review on the employed optimization methodologies to sustainability and the sustainable development including sustainable energy, sustainable buildings, and sustainable environment. Since energy optimization is one of the major necessities of sustainability, sustainable development is investigated from the energy perspective. In addition, the concept, definitions, and elements of the sustainability and optimization have been presented, and the review of the optimization metaheuristic algorithms used in recent published articles related to sustainability and sustainable development was carried out. Thus, it is believed that this paper can be appropriate, beneficial, and practical for students, academic researchers, engineers, and other professionals.

ACS Style

Ali Sadollah; Mohammad Nasir; Zong Woo Geem. Sustainability and Optimization: From Conceptual Fundamentals to Applications. Sustainability 2020, 12, 2027 .

AMA Style

Ali Sadollah, Mohammad Nasir, Zong Woo Geem. Sustainability and Optimization: From Conceptual Fundamentals to Applications. Sustainability. 2020; 12 (5):2027.

Chicago/Turabian Style

Ali Sadollah; Mohammad Nasir; Zong Woo Geem. 2020. "Sustainability and Optimization: From Conceptual Fundamentals to Applications." Sustainability 12, no. 5: 2027.

Conference paper
Published: 21 September 2019 in Advances in Intelligent Systems and Computing
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This paper proposes an enhanced harmony search algorithm for solving computationally expensive benchmarks widely used in the literature. We explored the potential and applicability of the original harmony search (HS) algorithm through introducing an extended version of the algorithm integrated with a new dynamic search equation enabling the algorithm to take guided larger steps at the beginning of the search. In the 4-Rule Harmony Search (4RHS), an extra rule is added to the standard HS without adding any user parameters to existing initial parameters. The 4RHS algorithm is then tested through optimal solving of different well-known and well-used benchmarks from classical to so CEC’ 2015 series, where the results of the 4RHS are compared with simple and improved version of HS algorithms as well as other optimization techniques. The obtained optimization results show the attractiveness of the added rule into the standard HS.

ACS Style

Ali Sadollah; Joong Hoon Kim; Young Hwan Choi; Negar Karamoddin. 4-Rule Harmony Search Algorithm for Solving Computationally Expensive Optimization Test Problems. Advances in Intelligent Systems and Computing 2019, 202 -209.

AMA Style

Ali Sadollah, Joong Hoon Kim, Young Hwan Choi, Negar Karamoddin. 4-Rule Harmony Search Algorithm for Solving Computationally Expensive Optimization Test Problems. Advances in Intelligent Systems and Computing. 2019; ():202-209.

Chicago/Turabian Style

Ali Sadollah; Joong Hoon Kim; Young Hwan Choi; Negar Karamoddin. 2019. "4-Rule Harmony Search Algorithm for Solving Computationally Expensive Optimization Test Problems." Advances in Intelligent Systems and Computing , no. : 202-209.

Journal article
Published: 08 August 2019 in Water
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Engineering benchmark problems with specific characteristics have been used to compare the performance and reliability of metaheuristic algorithms, and water distribution system design benchmarks are also widely used. However, only a few benchmark design problems have been considered in the research community. Due to the limited set of previous benchmarks, it is challenging to identify the algorithm with the best performance and the highest reliability among a group of algorithms. Therefore, in this study, a new water distribution system design benchmark problem generation method is proposed considering problem size and complexity modifications of a reference benchmark. The water distribution system design benchmark problems are used for performance and reliability comparison among several reported metaheuristic optimization algorithms. The optimal design results are able to quantify the performance and reliability of the compared algorithms which shows each metaheuristic algorithm has its own strengths and weaknesses. Finally, using the proposed method in this study, guidelines are derived for selecting an appropriate metaheuristic algorithm for water distribution system design.

ACS Style

Ho Min Lee; Donghwi Jung; Ali Sadollah; Do Guen Yoo; Joong Hoon Kim. Generation of Benchmark Problems for Optimal Design of Water Distribution Systems. Water 2019, 11, 1637 .

AMA Style

Ho Min Lee, Donghwi Jung, Ali Sadollah, Do Guen Yoo, Joong Hoon Kim. Generation of Benchmark Problems for Optimal Design of Water Distribution Systems. Water. 2019; 11 (8):1637.

Chicago/Turabian Style

Ho Min Lee; Donghwi Jung; Ali Sadollah; Do Guen Yoo; Joong Hoon Kim. 2019. "Generation of Benchmark Problems for Optimal Design of Water Distribution Systems." Water 11, no. 8: 1637.

Original article
Published: 16 January 2019 in Neural Computing and Applications
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In this article, a self-adaptive global mine blast algorithm (GMBA) is proposed for numerical optimization. This algorithm is designed in a novel way, and a new shrapnel equation is proposed for the exploitation phase of mine blast algorithm. A theoretical study is performed, which proves the convergence of any typical shrapnel piece; a new definition for parameters values is defined based on the performed theoretical studies. The promising nature of newly designed exploitation idea is verified with the help of multiple numerical experiments. A state-of-the-art set of benchmark problems are solved with the proposed GMBA, and the optimization results are compared with seven state-of-the-art optimization algorithms. The experimental results are statistically validated by using Wilcoxon signed-rank test, and time complexity of GMBA is also calculated. It has been justified that the proposed GMBA works as a global optimizer for constrained optimization problems. As an application to the newly developed GMBA, an important data clustering problem is solved on six data clusters and the clustering results are compared with the state-of-the-art optimization algorithms. The promising results claim the proposed GMBA as a strong optimizer for data clustering application.

ACS Style

Anupam Yadav; Ali Sadollah; Neha Yadav; J. H. Kim. Self-adaptive global mine blast algorithm for numerical optimization. Neural Computing and Applications 2019, 32, 2423 -2444.

AMA Style

Anupam Yadav, Ali Sadollah, Neha Yadav, J. H. Kim. Self-adaptive global mine blast algorithm for numerical optimization. Neural Computing and Applications. 2019; 32 (7):2423-2444.

Chicago/Turabian Style

Anupam Yadav; Ali Sadollah; Neha Yadav; J. H. Kim. 2019. "Self-adaptive global mine blast algorithm for numerical optimization." Neural Computing and Applications 32, no. 7: 2423-2444.

Original article
Published: 27 November 2018 in Journal of Experimental & Theoretical Artificial Intelligence
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Water cycle algorithm (WCA) is a population-based metaheuristic algorithm, inspired by the water cycle process and movement of rivers and streams towards sea. The WCA shows good performance in both exploration and exploitation phases. Further, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of WCA is proved theoretically. In this paper, CEC’15 computationally expensive benchmark problems (i.e., 15 problems) have been considered for efficiency measurement of WCA accompanied with other optimisers. Also, a new discretisation strategy for the WCA has been proposed and applied along with other optimisers for solving combinatorial Internet shopping optimisation problem. By applying complexity analysis, it shows that using the WCA intricacy from dimension 10–30 is increased for almost three times. Proposing a unique discretisation approach along with providing iterative convergence proof can be considered as novelty of this research. By observing the attained numerical results, the WCA could find the minimum average error of CEC’15 in 12 and 8 out of 15 cases for dimensions 10 and 30, respectively. Experimental optimisation results for a wide range computationally expensive problems reveal the effectiveness and advantage of WCA for solving both continuous and discrete optimisation problems.

ACS Style

Hassan Sayyaadi; Ali Sadollah; Anupam Yadav; Neha Yadav. Stability and iterative convergence of water cycle algorithm for computationally expensive and combinatorial Internet shopping optimisation problems. Journal of Experimental & Theoretical Artificial Intelligence 2018, 31, 701 -721.

AMA Style

Hassan Sayyaadi, Ali Sadollah, Anupam Yadav, Neha Yadav. Stability and iterative convergence of water cycle algorithm for computationally expensive and combinatorial Internet shopping optimisation problems. Journal of Experimental & Theoretical Artificial Intelligence. 2018; 31 (5):701-721.

Chicago/Turabian Style

Hassan Sayyaadi; Ali Sadollah; Anupam Yadav; Neha Yadav. 2018. "Stability and iterative convergence of water cycle algorithm for computationally expensive and combinatorial Internet shopping optimisation problems." Journal of Experimental & Theoretical Artificial Intelligence 31, no. 5: 701-721.

Book chapter
Published: 31 October 2018 in Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications
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ACS Style

Ali Sadollah. Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System? Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications 2018, 1 .

AMA Style

Ali Sadollah. Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System? Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications. 2018; ():1.

Chicago/Turabian Style

Ali Sadollah. 2018. "Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System?" Fuzzy Logic Based in Optimization Methods and Control Systems and its Applications , no. : 1.

Articles
Published: 15 October 2018 in Engineering Optimization
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Traffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration (i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion.

ACS Style

Ali Sadollah; Kaizhou Gao; Yicheng Zhang; Yi Zhang; Rong Su. Management of traffic congestion in adaptive traffic signals using a novel classification-based approach. Engineering Optimization 2018, 51, 1509 -1528.

AMA Style

Ali Sadollah, Kaizhou Gao, Yicheng Zhang, Yi Zhang, Rong Su. Management of traffic congestion in adaptive traffic signals using a novel classification-based approach. Engineering Optimization. 2018; 51 (9):1509-1528.

Chicago/Turabian Style

Ali Sadollah; Kaizhou Gao; Yicheng Zhang; Yi Zhang; Rong Su. 2018. "Management of traffic congestion in adaptive traffic signals using a novel classification-based approach." Engineering Optimization 51, no. 9: 1509-1528.

Conference paper
Published: 24 August 2018 in Advances in Intelligent Systems and Computing
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Various metaheuristic optimization algorithms are being developed and applied to find optimal solutions of real-world problems. Engineering benchmark problems have been often used for the performance comparison among metaheuristic algorithms, and water distribution system (WDS) design problem is one of the widely used benchmarks. However, only few traditional WDS design problems have been considered in the research community. Thus, it is very challenging to identify an algorithm’s better performance over other algorithms with such limited set of traditional benchmark problems of unknown characteristics. This study proposes an approach to generate WDS design benchmarks by changing five problem characteristic factors which are used to compare the performance of metaheuristic algorithms. Obtained optimization results show that WDS design benchmark problems generated with specific characteristic under control help identify the strength and weakness of reported algorithms. Finally, guidelines on the selection of a proper algorithm for WDS design problems are derived.

ACS Style

Ho Min Lee; Donghwi Jung; Ali Sadollah; Eui Hoon Lee; Joong Hoon Kim. Performance Comparison of Metaheuristic Optimization Algorithms Using Water Distribution System Design Benchmarks. Advances in Intelligent Systems and Computing 2018, 97 -104.

AMA Style

Ho Min Lee, Donghwi Jung, Ali Sadollah, Eui Hoon Lee, Joong Hoon Kim. Performance Comparison of Metaheuristic Optimization Algorithms Using Water Distribution System Design Benchmarks. Advances in Intelligent Systems and Computing. 2018; ():97-104.

Chicago/Turabian Style

Ho Min Lee; Donghwi Jung; Ali Sadollah; Eui Hoon Lee; Joong Hoon Kim. 2018. "Performance Comparison of Metaheuristic Optimization Algorithms Using Water Distribution System Design Benchmarks." Advances in Intelligent Systems and Computing , no. : 97-104.

Journal article
Published: 21 July 2018 in Applied Soft Computing
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In this research, a new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems. The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs. The NNA benefits from complicated structure of the ANNs and its operators in order to generate new candidate solutions. In terms of convergence proof, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of NNA is proved theoretically. In this paper, the NNA with its interconnected computing unit is examined for 21 well-known unconstrained benchmarks with dimensions 50 to 200 for evaluating its performance compared with the state-of-the-art algorithms and recent optimization methods. Besides, several constrained engineering design problems have been investigated to validate the efficiency of NNA for searching in feasible region in constrained optimization problems. Being an algorithm without any effort for fine tuning initial parameters and statistically superior can distinguish the NNA over other reported optimizers. It can be concluded that, the ANNs and its particular structure can be successfully utilized and modeled as metaheuristic optimization method for handling optimization problems.

ACS Style

Ali Sadollah; Hassan Sayyaadi; Anupam Yadav. A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing 2018, 71, 747 -782.

AMA Style

Ali Sadollah, Hassan Sayyaadi, Anupam Yadav. A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing. 2018; 71 ():747-782.

Chicago/Turabian Style

Ali Sadollah; Hassan Sayyaadi; Anupam Yadav. 2018. "A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm." Applied Soft Computing 71, no. : 747-782.

Journal article
Published: 04 July 2018 in Applied Soft Computing
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The water cycle algorithm (WCA) is a nature-inspired meta-heuristic recently contributed to the community in 2012, which finds its motivation in the natural surface runoff phase in water cycle process and on how streams and rivers flow into the sea. This method has been so far successfully applied to many engineering applications, spread over a wide variety of application fields. In this paper an enhanced discrete version of the WCA (coined as DWCA) is proposed for solving the Symmetric and Asymmetric Traveling Salesman Problem. Aimed at proving that the developed approach is a promising approximation method for solving this family of optimization problems, the designed solver has been tested over 33 problem datasets, comparing the obtained outcomes with the ones got by six different algorithmic counterparts from the related literature: genetic algorithm, island-based genetic algorithm, evolutionary simulated annealing, bat algorithm, firefly algorithm and imperialist competitive algorithm. Furthermore, the statistical significance of the performance gaps found in this benchmark is validated based on the results from non-parametric tests, not only in terms of optimality but also in regards to convergence speed. We conclude that the proposed DWCA approach outperforms – with statistical significance – any other optimization technique in the benchmark in terms of both computation metrics.

ACS Style

Eneko Osaba; Javier Del Ser; Ali Sadollah; Miren Nekane Bilbao; David Camacho. A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem. Applied Soft Computing 2018, 71, 277 -290.

AMA Style

Eneko Osaba, Javier Del Ser, Ali Sadollah, Miren Nekane Bilbao, David Camacho. A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem. Applied Soft Computing. 2018; 71 ():277-290.

Chicago/Turabian Style

Eneko Osaba; Javier Del Ser; Ali Sadollah; Miren Nekane Bilbao; David Camacho. 2018. "A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem." Applied Soft Computing 71, no. : 277-290.

Journal article
Published: 01 July 2018 in Applied Soft Computing
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ACS Style

Ali Sadollah; Hassan Sayyaadi; Do Guen Yoo; Ho Min Lee; Joong Hoon Kim. Mine blast harmony search: A new hybrid optimization method for improving exploration and exploitation capabilities. Applied Soft Computing 2018, 68, 548 -564.

AMA Style

Ali Sadollah, Hassan Sayyaadi, Do Guen Yoo, Ho Min Lee, Joong Hoon Kim. Mine blast harmony search: A new hybrid optimization method for improving exploration and exploitation capabilities. Applied Soft Computing. 2018; 68 ():548-564.

Chicago/Turabian Style

Ali Sadollah; Hassan Sayyaadi; Do Guen Yoo; Ho Min Lee; Joong Hoon Kim. 2018. "Mine blast harmony search: A new hybrid optimization method for improving exploration and exploitation capabilities." Applied Soft Computing 68, no. : 548-564.

Journal article
Published: 07 March 2018 in Sustainability
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Public transportation can have an efficient role ingainingtraveler satisfaction while decreasing operation costs through establishing an integrated public transit system. The main objective of this research is to propose an integrated multimodal transit model to design the best combination of both railway and feeder bus mode transit systems, while minimizing total cost. In this paper, we have proposed a strategy for designing transit networks that provide multimodal services at each stop, and for consecutively assigning optimum demand to the different feeder modes. Optimum transit networks have been achieved using single and multi-objective approaches via metaheuristic optimization algorithms, such as simulated annealing, genetic algorithms, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The used input data and study area were based on the real transit network of Petaling Jaya, located in Kuala Lumpur, Malaysia. Numerical results of the presented model, containing the statistical results, the optimum demand ratio, optimal solution, convergence rate, and comparisons among best solutions have been discussed in detail.

ACS Style

Mohammad Hadi Almasi; Ali Sadollah; Yoonseok Oh; Dong-Kyu Kim; Seungmo Kang. Optimal Coordination Strategy for an Integrated Multimodal Transit Feeder Network Design Considering Multiple Objectives. Sustainability 2018, 10, 734 .

AMA Style

Mohammad Hadi Almasi, Ali Sadollah, Yoonseok Oh, Dong-Kyu Kim, Seungmo Kang. Optimal Coordination Strategy for an Integrated Multimodal Transit Feeder Network Design Considering Multiple Objectives. Sustainability. 2018; 10 (3):734.

Chicago/Turabian Style

Mohammad Hadi Almasi; Ali Sadollah; Yoonseok Oh; Dong-Kyu Kim; Seungmo Kang. 2018. "Optimal Coordination Strategy for an Integrated Multimodal Transit Feeder Network Design Considering Multiple Objectives." Sustainability 10, no. 3: 734.