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The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.
Sergio Alonso; Rosana Montes; Daniel Molina; Iván Palomares; Eugenio Martínez-Cámara; Manuel Chiachio; Juan Chiachio; Francisco Melero; Pablo García-Moral; Bárbara Fernández; Cristina Moral; Rosario Marchena; Javier Pérez de Vargas; Francisco Herrera. Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability 2021, 13, 6038 .
AMA StyleSergio Alonso, Rosana Montes, Daniel Molina, Iván Palomares, Eugenio Martínez-Cámara, Manuel Chiachio, Juan Chiachio, Francisco Melero, Pablo García-Moral, Bárbara Fernández, Cristina Moral, Rosario Marchena, Javier Pérez de Vargas, Francisco Herrera. Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability. 2021; 13 (11):6038.
Chicago/Turabian StyleSergio Alonso; Rosana Montes; Daniel Molina; Iván Palomares; Eugenio Martínez-Cámara; Manuel Chiachio; Juan Chiachio; Francisco Melero; Pablo García-Moral; Bárbara Fernández; Cristina Moral; Rosario Marchena; Javier Pérez de Vargas; Francisco Herrera. 2021. "Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys." Sustainability 13, no. 11: 6038.
In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.
Daniel Molina; Javier Poyatos; Javier Del Ser; Salvador García; Amir Hussain; Francisco Herrera. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation 2020, 12, 897 -939.
AMA StyleDaniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation. 2020; 12 (5):897-939.
Chicago/Turabian StyleDaniel Molina; Javier Poyatos; Javier Del Ser; Salvador García; Amir Hussain; Francisco Herrera. 2020. "Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations." Cognitive Computation 12, no. 5: 897-939.
Javier Del Ser; Eneko Osaba; Daniel Molina; Xin-She Yang; Sancho Salcedo-Sanz; David Camacho; Swagatam Das; Ponnuthurai N. Suganthan; Carlos Coello Coello; Francisco Herrera. Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation 2019, 48, 220 -250.
AMA StyleJavier Del Ser, Eneko Osaba, Daniel Molina, Xin-She Yang, Sancho Salcedo-Sanz, David Camacho, Swagatam Das, Ponnuthurai N. Suganthan, Carlos Coello Coello, Francisco Herrera. Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation. 2019; 48 ():220-250.
Chicago/Turabian StyleJavier Del Ser; Eneko Osaba; Daniel Molina; Xin-She Yang; Sancho Salcedo-Sanz; David Camacho; Swagatam Das; Ponnuthurai N. Suganthan; Carlos Coello Coello; Francisco Herrera. 2019. "Bio-inspired computation: Where we stand and what's next." Swarm and Evolutionary Computation 48, no. : 220-250.
Over the recent years, continuous optimization has significantly evolved to become the mature research field it is nowadays. Through this process, evolutionary algorithms had an important role, as they are able to obtain good results with limited resources. Among them, bio-inspired algorithms, which mimic cooperative and competitive behaviors observed in animals, are a very active field, with more proposals every year. This increment in the number of optimization algorithms is apparent in the many competitions held at corresponding special sessions in the last 10 years. In these competitions, several algorithms or ideas have become points of reference, and used as starting points for more advanced algorithms in following competitions. In this paper, we have obtained, for different real-parameter competitions, their benchmarks, participants, and winners (from the competitions’ website) and we review the most relevant algorithms and techniques, presenting the trajectory they have followed over the last years and how some of these works have deeply influenced the top performing algorithms of today. The aim is to be both a useful reference for researchers new to this interesting research topic and a useful guide for current researchers in the field. We have observed that there are several algorithms, like the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE), Mean-Variance Mapping Optimization (MVMO), and Multiple Offspring Sampling (MOS), which have obtained a strong influence over other algorithms. We have also suggested several techniques that are being widely adopted among the winning proposals, and that could be used for more competitive algorithms. Global optimization is a mature research field in continuous improvement, and the history of competitions provides useful information about the past that can help us to learn how to go forward in the future.
Daniel Molina; Antonio Latorre; Francisco Herrera. An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions. Cognitive Computation 2018, 10, 517 -544.
AMA StyleDaniel Molina, Antonio Latorre, Francisco Herrera. An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions. Cognitive Computation. 2018; 10 (4):517-544.
Chicago/Turabian StyleDaniel Molina; Antonio Latorre; Francisco Herrera. 2018. "An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions." Cognitive Computation 10, no. 4: 517-544.
In this paper we propose a specially designed memetic algorithm for multimodal optimisation problems. The proposal uses a niching strategy, called region-based niching strategy, that divides the search space in predefined and indexable hypercubes with decreasing size, called regions. This niching technique allows our proposal to keep high diversity in the population, and to keep the most promising regions in an external archive. The most promising solutions are improved with a local search method and also stored in the archive. The archive is used as an index to effiently prevent further exploration of these areas with the evolutionary algorithm. The resulting algorithm, called Region-based Memetic Algorithm with Archive, is tested on the benchmark proposed in the special session and competition on niching methods for multimodal function optimisation of the Congress on Evolutionary Computation in 2013. The results obtained show that the region-based niching strategy is more efficient than the classical niching strategy called clearing and that the use of the archive as restrictive index significantly improves the exploration efficiency of the algorithm. The proposal achieves better exploration and accuracy than other existing techniques.
Benjamin Lacroix; Daniel Molina; Francisco Herrera. Region-based memetic algorithm with archive for multimodal optimisation. Information Sciences 2016, 367-368, 719 -746.
AMA StyleBenjamin Lacroix, Daniel Molina, Francisco Herrera. Region-based memetic algorithm with archive for multimodal optimisation. Information Sciences. 2016; 367-368 ():719-746.
Chicago/Turabian StyleBenjamin Lacroix; Daniel Molina; Francisco Herrera. 2016. "Region-based memetic algorithm with archive for multimodal optimisation." Information Sciences 367-368, no. : 719-746.
Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called “variable mesh optimization” (VMO), in which the set of nodes (potential solutions) are distributed as a mesh. This mesh is variable, because it evolves to maintain a controlled diversity (avoiding solutions too close to each other) and to guide it to the best solutions (by a mechanism of resampling from current nodes to its best neighbour). This proposal is compared with basic population-based meta-heuristics using a benchmark of multimodal continuous functions, showing that VMO is a competitive algorithm.
Amilkar Puris; Rafael Bello; Daniel Molina; Francisco Herrera; Daniel Molina Cabrera. Variable mesh optimization for continuous optimization problems. Soft Computing 2011, 16, 511 -525.
AMA StyleAmilkar Puris, Rafael Bello, Daniel Molina, Francisco Herrera, Daniel Molina Cabrera. Variable mesh optimization for continuous optimization problems. Soft Computing. 2011; 16 (3):511-525.
Chicago/Turabian StyleAmilkar Puris; Rafael Bello; Daniel Molina; Francisco Herrera; Daniel Molina Cabrera. 2011. "Variable mesh optimization for continuous optimization problems." Soft Computing 16, no. 3: 511-525.
This editorial note presents the motivations, objectives, and structure of the special issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. In addition, it provides the link to an associated Website where complementary material to the special issue is available.
M. Lozano; D. Molina; Francisco Herrera; Daniel Molina Cabrera. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Computing 2010, 15, 2085 -2087.
AMA StyleM. Lozano, D. Molina, Francisco Herrera, Daniel Molina Cabrera. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Computing. 2010; 15 (11):2085-2087.
Chicago/Turabian StyleM. Lozano; D. Molina; Francisco Herrera; Daniel Molina Cabrera. 2010. "Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems." Soft Computing 15, no. 11: 2085-2087.