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Dr. Peng-Yeng Yin
Department of Information Management, National Chi Nan University, Puli, Taiwan

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0 Artificial Intelligence
0 Computational Intelligence
0 Evolutionary Computation
0 Image Processing
0 Machine Learning

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Metaheuristics
Wind and Solar Energy

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Journal article
Published: 28 February 2021 in Sustainability
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It is estimated that 1 billion waste tires are generated every year across the globe, yet only 10% are being processed, and much rubber waste is yielded during manufacturing. These waste tires and rubber scraps are poisonous to the environment when processed via incineration and landfill. Rubber circular manufacturing is an effective solution that reduces not only rubber waste but also raw material costs. In this paper we propose a two-line flowshop model for the circular rubber manufacturing problem (CRMP), where the job sequence of two production lines is appropriately aligned to obtain the shortest makespan while guaranteeing that sufficient rubber waste yielded in the first line is ready to be reused for circular production in the second line. A genetic algorithm (GA) is developed, and the design of its genetic operations is customized to the CRMP context to achieve efficient and effective evolution. The experimental results with both real and synthetic datasets show that the GA significantly surpasses two heuristics in the literature by delivering the minimum makespan, which is 3.4 to 11.2% shorter than those obtained by the two competing methods.

ACS Style

Peng-Yeng Yin; Hsin-Min Chen; Yi-Lung Cheng; Ying-Chieh Wei; Ya-Lin Huang; Rong-Fuh Day. Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing. Sustainability 2021, 13, 2576 .

AMA Style

Peng-Yeng Yin, Hsin-Min Chen, Yi-Lung Cheng, Ying-Chieh Wei, Ya-Lin Huang, Rong-Fuh Day. Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing. Sustainability. 2021; 13 (5):2576.

Chicago/Turabian Style

Peng-Yeng Yin; Hsin-Min Chen; Yi-Lung Cheng; Ying-Chieh Wei; Ya-Lin Huang; Rong-Fuh Day. 2021. "Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing." Sustainability 13, no. 5: 2576.

Journal article
Published: 15 December 2020 in Applied Sciences
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Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

ACS Style

Peng-Yeng Yin; Po-Yen Chen; Ying-Chieh Wei; Rong-Fuh Day. Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization. Applied Sciences 2020, 10, 8961 .

AMA Style

Peng-Yeng Yin, Po-Yen Chen, Ying-Chieh Wei, Rong-Fuh Day. Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization. Applied Sciences. 2020; 10 (24):8961.

Chicago/Turabian Style

Peng-Yeng Yin; Po-Yen Chen; Ying-Chieh Wei; Rong-Fuh Day. 2020. "Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization." Applied Sciences 10, no. 24: 8961.

Journal article
Published: 30 September 2020 in Symmetry
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Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.

ACS Style

Ray-I Chang; Chao-Lung Ting; Syuan-Yi Wu; Peng-Yeng Yin. Context-Dependent Object Proposal and Recognition. Symmetry 2020, 12, 1619 .

AMA Style

Ray-I Chang, Chao-Lung Ting, Syuan-Yi Wu, Peng-Yeng Yin. Context-Dependent Object Proposal and Recognition. Symmetry. 2020; 12 (10):1619.

Chicago/Turabian Style

Ray-I Chang; Chao-Lung Ting; Syuan-Yi Wu; Peng-Yeng Yin. 2020. "Context-Dependent Object Proposal and Recognition." Symmetry 12, no. 10: 1619.

Journal article
Published: 12 May 2020 in Renewable Energy
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Wind power and solar photovoltaic (PV) power are two of the most implemented renewable energy; however, they are vulnerable to weather uncertainties. Most existing works developed energy models by estimating the mean production, overlooking the risk that some undesired productions with non-negligible probabilities could be greatly deviated from the mean. Moreover, current hybrid wind-solar energy models lack deliberation on the co-location interferences which spoil the production. Considering weather uncertainties and co-location interferences, a flexible and applicable hybrid energy model is demanded. This paper addresses these underexplored yet important issues. First, we apply Monte Carlo method for simulating uncertain wind, sky and dust conditions based on multi-year real-world data. Secondly, the wind-solar farm co-location interferences, such as wind-turbine’s shade and penumbra on PV surfaces, forbidden area for maintenance roads, and land-usage conflict, are deliberately modeled. Thirdly, we propose five risk-aware energy optimization (RAEO) models which provide various metrics for evaluating production benefit, cost, and risk. The experimental results show that our RAEO models can deal with weather uncertainties and co-location interferences and are flexible enough to provide various forms of analytics as responses to decision-maker’s needs.

ACS Style

Peng-Yeng Yin; Chun-Ying Cheng; Hsin-Min Chen; Tsai-Hung Wu. Risk-aware optimal planning for a hybrid wind-solar farm. Renewable Energy 2020, 157, 290 -302.

AMA Style

Peng-Yeng Yin, Chun-Ying Cheng, Hsin-Min Chen, Tsai-Hung Wu. Risk-aware optimal planning for a hybrid wind-solar farm. Renewable Energy. 2020; 157 ():290-302.

Chicago/Turabian Style

Peng-Yeng Yin; Chun-Ying Cheng; Hsin-Min Chen; Tsai-Hung Wu. 2020. "Risk-aware optimal planning for a hybrid wind-solar farm." Renewable Energy 157, no. : 290-302.

Journal article
Published: 01 January 2019 in Mathematical Biosciences and Engineering
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He PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively.

ACS Style

Peng-Yeng Yin; Chih-Chun Tsai; Rong-Fuh Day; Ching-Ying Tung; Bir Bhanu. Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors. Mathematical Biosciences and Engineering 2019, 16, 6858 -6873.

AMA Style

Peng-Yeng Yin, Chih-Chun Tsai, Rong-Fuh Day, Ching-Ying Tung, Bir Bhanu. Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors. Mathematical Biosciences and Engineering. 2019; 16 (6):6858-6873.

Chicago/Turabian Style

Peng-Yeng Yin; Chih-Chun Tsai; Rong-Fuh Day; Ching-Ying Tung; Bir Bhanu. 2019. "Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors." Mathematical Biosciences and Engineering 16, no. 6: 6858-6873.

Journal article
Published: 03 July 2018 in Applied Soft Computing
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Effective demand forecasting is essential for regulating power distribution, scheduling production, and initiating new energy projects. Existing forecasting models have contrasting features and manifest various types of errors. This paper proposes a multi-predictor approach which applies reinforcement learning for selecting the fittest predictors to enhance the collaborative performance. A new univariate predictor is developed based on the Gaussian mixture and phase shifting and rescaling techniques, and two multivariate predictors are developed from a landscape analysis with potential econometrics. Each individual predictor is trained by the cyber swarm algorithm (CSA) to find the optimal parameter values. The 10-fold cross validation for regression parameter optimization by using CSA and the constriction factor particle swarm optimization (CFPSO) shows the effectiveness of the former against the latter. Our reinforcement learning forecasting method is able to automatically select the best predictor to perform at various time instances and allow the embedding predictors to complement one another. Our experimental results experimented with Taiwan’s electricity demand time series during 2001 to 2014 show that the prediction improvement contributed by the proposed approach over the original individual predictors is significant in terms of the mean absolute percentage error (MAPE) and the mean square error (MSE).

ACS Style

Peng-Yeng Yin; Ching-Hui Chao. Automatic selection of fittest energy demand predictors based on cyber swarm optimization and reinforcement learning. Applied Soft Computing 2018, 71, 152 -164.

AMA Style

Peng-Yeng Yin, Ching-Hui Chao. Automatic selection of fittest energy demand predictors based on cyber swarm optimization and reinforcement learning. Applied Soft Computing. 2018; 71 ():152-164.

Chicago/Turabian Style

Peng-Yeng Yin; Ching-Hui Chao. 2018. "Automatic selection of fittest energy demand predictors based on cyber swarm optimization and reinforcement learning." Applied Soft Computing 71, no. : 152-164.

Conference paper
Published: 16 June 2018 in Privacy Enhancing Technologies
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The ascending of quantity of CO2 emissions is the main factor contributing the global warming which results in extremely abnormal weather and causes disaster damages. Due to intensive CO2 pollutants produced by classic energy sources such as fossil fuels, practitioners and researchers pay increasing attentions on the renewable energy production such as wind power. Optimal wind turbine placement problem is to find the optimal number and placement location of wind turbines in a wind farm against the wake effect. The efficiency of wind power production does not necessarily grows with an increasing number of installed wind turbines. This paper presents a hyper-heuristic framework combining several lower-level heuristics with an artificial bee colony algorithm and a simulated annealing technique to construct an optimal wind turbine placement considering wake effect influence. Finally, we compare our approach with existing works in the literature. The experimental results show that our approach produces the wind power with a lower cost of energy.

ACS Style

Peng-Yeng Yin; Geng-Shi Li. A Hyper-Heuristic of Artificial Bee Colony and Simulated Annealing for Optimal Wind Turbine Placement. Privacy Enhancing Technologies 2018, 145 -152.

AMA Style

Peng-Yeng Yin, Geng-Shi Li. A Hyper-Heuristic of Artificial Bee Colony and Simulated Annealing for Optimal Wind Turbine Placement. Privacy Enhancing Technologies. 2018; ():145-152.

Chicago/Turabian Style

Peng-Yeng Yin; Geng-Shi Li. 2018. "A Hyper-Heuristic of Artificial Bee Colony and Simulated Annealing for Optimal Wind Turbine Placement." Privacy Enhancing Technologies , no. : 145-152.

Journal article
Published: 01 December 2017 in Energy
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ACS Style

Peng-Yeng Yin; Tsai-Hung Wu; Ping-Yi Hsu. Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D. Energy 2017, 141, 579 -597.

AMA Style

Peng-Yeng Yin, Tsai-Hung Wu, Ping-Yi Hsu. Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D. Energy. 2017; 141 ():579-597.

Chicago/Turabian Style

Peng-Yeng Yin; Tsai-Hung Wu; Ping-Yi Hsu. 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D." Energy 141, no. : 579-597.

Conference paper
Published: 24 June 2017 in Privacy Enhancing Technologies
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Wind farm micro-siting is to determine the optimal placement for the wind turbines such that the cost of energy (COE) is minimal. The problem is clearly a long-term decision one, once the micro-siting was constructed, it is extremely costly to reconfigure the layout. Long-term electricity demand forecasting is a necessity for formation of governmental energy policy. We anticipate that the two problems should be considered simultaneously to create potential benefits because they have resembling properties and close supply-and-demand relationship. This paper proposes a demand-aware micro-siting system to COE minimization. The system is a holistic integration of long-term electricity demand forecasting and optimal micro-siting. A case study in central Taiwan area is conducted to validate the feasibility of the proposed system.

ACS Style

Peng-Yeng Yin; Ching-Hui Chao; Tsai-Hung Wu; Ping-Yi Hsu. Optimal Micro-siting Planning Considering Long-Term Electricity Demand. Privacy Enhancing Technologies 2017, 445 -453.

AMA Style

Peng-Yeng Yin, Ching-Hui Chao, Tsai-Hung Wu, Ping-Yi Hsu. Optimal Micro-siting Planning Considering Long-Term Electricity Demand. Privacy Enhancing Technologies. 2017; ():445-453.

Chicago/Turabian Style

Peng-Yeng Yin; Ching-Hui Chao; Tsai-Hung Wu; Ping-Yi Hsu. 2017. "Optimal Micro-siting Planning Considering Long-Term Electricity Demand." Privacy Enhancing Technologies , no. : 445-453.

Journal article
Published: 01 May 2017 in Applied Soft Computing
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ACS Style

Peng-Yeng Yin; Tsai-Hung Wu. Multi-objective and multi-level image thresholding based on dominance and diversity criteria. Applied Soft Computing 2017, 54, 62 -73.

AMA Style

Peng-Yeng Yin, Tsai-Hung Wu. Multi-objective and multi-level image thresholding based on dominance and diversity criteria. Applied Soft Computing. 2017; 54 ():62-73.

Chicago/Turabian Style

Peng-Yeng Yin; Tsai-Hung Wu. 2017. "Multi-objective and multi-level image thresholding based on dominance and diversity criteria." Applied Soft Computing 54, no. : 62-73.

Journal article
Published: 01 November 2016 in Applied Mathematical Modelling
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Cross-docking distribution is becoming increasingly prevalent in supply chain management (SCM) due to the replacement of storage and order-picking with efficient product consolidation. Several successful cross-docking applications have been put into practice by companies such as Wal-Mart, FedEx, and Home Depot. Cross-docking consists of interrelated operations which require proper synchronization. Only a few early attempts have addressed the vehicle routing problem in this context. Moreover, the social consciousness of sustainable development has brought up the issue of green SCM which is not only environment-friendly but also beneficial to business values. This paper first addresses a previously studied problem and then proposes a new problem version for planning the least cost green vehicle routing in which the deployed vehicles transport the final products from suppliers to customers through a cross-dock subject to a CO2 intensity constraint. We develop an adaptive memory artificial bee colony (AMABC) algorithm to tackle both problems. Compared to a tabu search proposed in the literature, the AMABC algorithm can reach higher fuel efficiency by managing the loading along the route and yield less cost and CO2 intensities. Statistical tests of simulation and geographic data show that the AMABC method is robust against the problem size and convergence of the objective value is guaranteed with high confidence.

ACS Style

Peng-Yeng Yin; Ya-Lan Chuang. Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking. Applied Mathematical Modelling 2016, 40, 9302 -9315.

AMA Style

Peng-Yeng Yin, Ya-Lan Chuang. Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking. Applied Mathematical Modelling. 2016; 40 (21-22):9302-9315.

Chicago/Turabian Style

Peng-Yeng Yin; Ya-Lan Chuang. 2016. "Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking." Applied Mathematical Modelling 40, no. 21-22: 9302-9315.

Journal article
Published: 01 November 2016 in Applied Soft Computing
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Display Omitted The proposed HMTS algorithm can find hidden decision strategies by clustering the eye-movement sequences from users.HMTS uses adaptive memory programming and incorporates multi-start and local search strategies for global optimization.HMTS outperforms standard TS, GA, PSO, and K-means algorithms on both empirical and synthetic eye-movement datasets.The scalability and robustness analyses for HMTS have been conducted through a series of statistical tests. It is known that the decision strategy performed by a subject is implicit in his/her external behaviors. Eye movement is one of the observable external behaviors when humans are performing decision activities. Due to the dramatic increase of e-commerce volume on WWW, it is beneficial for the companies to know where the customers focus their attention on the webpage in deciding to make a purchase. This study proposes a new hybrid multi-start tabu search (HMTS) algorithm for finding the hidden decision strategies by clustering the eye-movement data obtained during the decision activities. The HMTS uses adaptive memory and employs both multi-start and local search strategies. An empirical dataset containing 294 eye-fixation sequences and a synthetic dataset consisting of 360 sequences were experimented with. We conduct the Sign test and the result shows that the proposed HMTS method significantly outperforms its variants which implement just one strategy, and the HMTS algorithm shows an improvement over genetic algorithm, particle swarm optimization, and K-means, with a level of significance α=0.01. The scalability and robustness of the HMTS is validated through a series of statistical tests.

ACS Style

Rong-Fuh Day; Peng-Yeng Yin; Yu-Chi Wang; Ching-Hui Chao. A new hybrid multi-start tabu search for finding hidden purchase decision strategies in WWW based on eye-movements. Applied Soft Computing 2016, 48, 217 -229.

AMA Style

Rong-Fuh Day, Peng-Yeng Yin, Yu-Chi Wang, Ching-Hui Chao. A new hybrid multi-start tabu search for finding hidden purchase decision strategies in WWW based on eye-movements. Applied Soft Computing. 2016; 48 ():217-229.

Chicago/Turabian Style

Rong-Fuh Day; Peng-Yeng Yin; Yu-Chi Wang; Ching-Hui Chao. 2016. "A new hybrid multi-start tabu search for finding hidden purchase decision strategies in WWW based on eye-movements." Applied Soft Computing 48, no. : 217-229.

Original article
Published: 13 September 2016 in Neural Computing and Applications
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Adaptive human–computer interfaces (HCIs) are fundamental to designing adaptive websites and adaptive decision support systems. Integrating these intelligent systems with modern eye trackers provides more effective ways to exploit eye fixation data and offers improved services to users. We develop an exemplar-based classifier using the tabu search algorithm to predict which decision strategy may underlie an empirical search behavior. Our algorithm reduces the size of decision concept representations to find the best exemplars for each concept. Experimental results show that our classifier is highly accurate in classifying the sequence of empirical eye fixations, demonstrating the promise of integrating adaptive HCIs with modern eye trackers.

ACS Style

Peng-Yeng Yin; Rong-Fuh Day; Yu-Chi Wang. Tabu search-based classification for eye-movement behavioral decisions. Neural Computing and Applications 2016, 29, 1433 -1443.

AMA Style

Peng-Yeng Yin, Rong-Fuh Day, Yu-Chi Wang. Tabu search-based classification for eye-movement behavioral decisions. Neural Computing and Applications. 2016; 29 (5):1433-1443.

Chicago/Turabian Style

Peng-Yeng Yin; Rong-Fuh Day; Yu-Chi Wang. 2016. "Tabu search-based classification for eye-movement behavioral decisions." Neural Computing and Applications 29, no. 5: 1433-1443.

Journal article
Published: 02 March 2016 in Engineering Applications of Artificial Intelligence
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Cross-docking technology transships products from incoming vehicles directly to outgoing vehicles by using the warehouse as a temporary buffer instead of a place for storage and retrieval. The supply chain management (SCM) with cross-docks is both effective and efficient where no storage is facilitated at the cross-dock and the order-picking is replaced by fast consolidation. However, cross-docking involves interrelated operations such as vehicle routing and vehicle scheduling which require proper planning and synchronization. Traditional cross-docking methods treat the operations separately and overlook the potential advantage of cooperative planning. This paper proposes a bi-objective mathematical formulation for the cross-docking with the noted new challenges. As the addressed problem is highly constrained, we develop a cooperative coevolution approach consisting of Hyper-heuristics and Hybrid-heuristics for achieving continuous improvement in alternating objectives. The performance of our approach is illustrated with real geographical data and is compared with existing models. Statistical tests based on intensive simulations, including the convergence 95% confidence analysis and the worst-case analysis, are conducted to provide reliable performance guarantee.

ACS Style

Peng-Yeng Yin; Sin-Ru Lyu; Ya-Lan Chuang. Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering. Engineering Applications of Artificial Intelligence 2016, 52, 40 -53.

AMA Style

Peng-Yeng Yin, Sin-Ru Lyu, Ya-Lan Chuang. Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering. Engineering Applications of Artificial Intelligence. 2016; 52 ():40-53.

Chicago/Turabian Style

Peng-Yeng Yin; Sin-Ru Lyu; Ya-Lan Chuang. 2016. "Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering." Engineering Applications of Artificial Intelligence 52, no. : 40-53.

Journal article
Published: 01 February 2016 in Applied Mathematical Modelling
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ACS Style

Peng-Yeng Yin; Tsai-Hung Wu; Ping-Yi Hsu. A power-deficiency and risk-management model for wind farm micro-siting using cyber swarm algorithm. Applied Mathematical Modelling 2016, 40, 2177 -2189.

AMA Style

Peng-Yeng Yin, Tsai-Hung Wu, Ping-Yi Hsu. A power-deficiency and risk-management model for wind farm micro-siting using cyber swarm algorithm. Applied Mathematical Modelling. 2016; 40 (3):2177-2189.

Chicago/Turabian Style

Peng-Yeng Yin; Tsai-Hung Wu; Ping-Yi Hsu. 2016. "A power-deficiency and risk-management model for wind farm micro-siting using cyber swarm algorithm." Applied Mathematical Modelling 40, no. 3: 2177-2189.

Journal article
Published: 30 April 2014 in Journal of Network and Computer Applications
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Quality-of-service (QoS) multicast routing is essential to many network applications such as IPTV, Internet radio, multimedia broadcasting, and real-time telecommunication. Recently, the minimum-cost multicast tree with the delay-and-bandwidth constraint (MinC/DB) problem in QoS multicast routing has caused the most attention. As the MinC/DB problem can be reduced to the constrained Steiner tree problem which has been shown to be NP-complete, it is very unlikely that a polynomial time algorithm would exist. In this paper, we propose a niched ant colony optimization with colony guides (NACOg) algorithm to tackle the MinC/DB problem. The NACOg algorithm first deliberates a constrained tree traversal (CTT) strategy that guarantees the search to any feasible trees with respect to the QoS constraints. The proposed CTT strategy employs adaptive memory structure as contemplated in the tabu search and the strategy is more effective in constraint-handling than both the penalty-function and the produce-and-repair strategies which were broadly used in the literature. The evolutionary optimization of the NACOg algorithm is empowered by the search balance between diversification and intensification. The diversification search is practiced by the individual niche-colony with its own pheromone matrix, and the intensification search is facilitated by the learning scheme which refers to the experience obtained by the niche-colony guide and the entire-colony guide. The experimental results on 100 problem instances have shown that the proposed NACOg algorithm produces the least cost QoS multicast trees compared to those obtained by the Haghighat genetic algorithm and the well-known KPP heuristic.

ACS Style

Peng-Yeng Yin; Ray-I. Chang; Chih-Chiang Chao; Yen-Ting Chu. Niched ant colony optimization with colony guides for QoS multicast routing. Journal of Network and Computer Applications 2014, 40, 61 -72.

AMA Style

Peng-Yeng Yin, Ray-I. Chang, Chih-Chiang Chao, Yen-Ting Chu. Niched ant colony optimization with colony guides for QoS multicast routing. Journal of Network and Computer Applications. 2014; 40 ():61-72.

Chicago/Turabian Style

Peng-Yeng Yin; Ray-I. Chang; Chih-Chiang Chao; Yen-Ting Chu. 2014. "Niched ant colony optimization with colony guides for QoS multicast routing." Journal of Network and Computer Applications 40, no. : 61-72.

Book chapter
Published: 01 January 2014 in Advances in Intelligent Systems and Computing
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Program module allocation problem (PMAP) is an important application of the quadratic assignment problem (QAP), which has been shown to be NPcomplete. The aim of the PMAP is to allocate a package of program modules to a number of distributed processors such that the incurred cost is minimal subject to specified resource constraints. We propose to employ a new metaheuristic, the cyber swarm algorithm (CSA), for finding the near optimal solution with reasonable time. The CSA has previously manifested excellent performance on solving continuous optimization problems. Our experimental results show that the CSA is more effective and efficient than modifications of genetic algorithm, particle swarm optimization, and harmony search in tackling the PMAP.

ACS Style

Peng-Yeng Yin; Pei-Pei Wang. A Cyber Swarm Algorithm for Constrained Program Module Allocation Problem. Advances in Intelligent Systems and Computing 2014, 245, 163 -172.

AMA Style

Peng-Yeng Yin, Pei-Pei Wang. A Cyber Swarm Algorithm for Constrained Program Module Allocation Problem. Advances in Intelligent Systems and Computing. 2014; 245 ():163-172.

Chicago/Turabian Style

Peng-Yeng Yin; Pei-Pei Wang. 2014. "A Cyber Swarm Algorithm for Constrained Program Module Allocation Problem." Advances in Intelligent Systems and Computing 245, no. : 163-172.

Research article
Published: 29 October 2013 in Mathematical Problems in Engineering
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The estimation of distribution algorithm (EDA) aims to explicitly model the probability distribution of the quality solutions to the underlying problem. By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions. In contrast to classic evolutionary algorithms (EAs), EDA framework is flexible and is able to handle inter variable dependence, which usually imposes difficulties on classic EAs. The success of EDA relies on effective and efficient building of the probability model. This paper facilitates EDA from the adaptive memory programming (AMP) domain which has developed several improved forms of EAs using the Cyber-EA framework. The experimental result on benchmark TSP instances supports our anticipation that the AMP strategies can enhance the performance of classic EDA by deriving a better approximation for the true distribution of the target solutions.

ACS Style

Peng-Yeng Yin; Hsi-Li Wu. Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming. Mathematical Problems in Engineering 2013, 2013, 1 -11.

AMA Style

Peng-Yeng Yin, Hsi-Li Wu. Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming. Mathematical Problems in Engineering. 2013; 2013 (1):1-11.

Chicago/Turabian Style

Peng-Yeng Yin; Hsi-Li Wu. 2013. "Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming." Mathematical Problems in Engineering 2013, no. 1: 1-11.

Journal article
Published: 21 June 2013 in Applied Mathematics and Computation
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With the rapid development in World Wide Web (WWW) technology, the number of webpages and the volume of information content have been overwhelming. It becomes increasingly important to help users find relevant webpage and information more easily and quickly. This situation causes widespread attention in constructing adaptive websites which automatically reorganize the structure or content by learning from the users’ browsing behaviors, as such the usage of the websites is improved. In this study we propose a new formulation for the website structure optimization (WSO) problem based on a comprehensive survey of existing works and practice considerations. An enhanced tabu search (ETS) algorithm is proposed with advanced search features of multiple neighborhoods, adaptive tabu lists, dynamic tabu tenure, and multi-level aspiration criteria. The experimental result on 24 real-world problem instances shows that the proposed ETS algorithm can obtain a better value of web usage estimation than a genetic algorithm method. Moreover, ETS is computationally efficient due to the strategy that handles problem constraints on-the-fly when constructing the solution.

ACS Style

Peng-Yeng Yin; Yi-Ming Guo. Optimization of multi-criteria website structure based on enhanced tabu search and web usage mining. Applied Mathematics and Computation 2013, 219, 11082 -11095.

AMA Style

Peng-Yeng Yin, Yi-Ming Guo. Optimization of multi-criteria website structure based on enhanced tabu search and web usage mining. Applied Mathematics and Computation. 2013; 219 (24):11082-11095.

Chicago/Turabian Style

Peng-Yeng Yin; Yi-Ming Guo. 2013. "Optimization of multi-criteria website structure based on enhanced tabu search and web usage mining." Applied Mathematics and Computation 219, no. 24: 11082-11095.

Journal article
Published: 05 July 2012 in Renewable Energy
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ACS Style

Peng-Yeng Yin; Tai-Yuan Wang. A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms. Renewable Energy 2012, 48, 489 -498.

AMA Style

Peng-Yeng Yin, Tai-Yuan Wang. A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms. Renewable Energy. 2012; 48 ():489-498.

Chicago/Turabian Style

Peng-Yeng Yin; Tai-Yuan Wang. 2012. "A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms." Renewable Energy 48, no. : 489-498.