Journal Article10.1109/TCYB.2014.2337117
MOMMOP: Multiobjective Optimization for Locating Multiple Optimal Solutions of Multimodal Optimization Problems
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TL;DR: A novel transformation technique based on multiobjective optimization for MMOPs, called MOMMOP, which transforms an MMOP into a multi objective optimization problem with two conflicting objectives, and which outperforms a number of methods for multimodal optimization.
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Abstract: In the field of evolutionary computation, there has been a growing interest in applying evolutionary algorithms to solve multimodal optimization problems (MMOPs). Due to the fact that an MMOP involves multiple optimal solutions, many niching methods have been suggested and incorporated into evolutionary algorithms for locating such optimal solutions in a single run. In this paper, we propose a novel transformation technique based on multiobjective optimization for MMOPs, called MOMMOP. MOMMOP transforms an MMOP into a multiobjective optimization problem with two conflicting objectives. After the above transformation, all the optimal solutions of an MMOP become the Pareto optimal solutions of the transformed problem. Thus, multiobjective evolutionary algorithms can be readily applied to find a set of representative Pareto optimal solutions of the transformed problem, and as a result, multiple optimal solutions of the original MMOP could also be simultaneously located in a single run. In principle, MOMMOP is an implicit niching method. In this paper, we also discuss two issues in MOMMOP and introduce two new comparison criteria. MOMMOP has been used to solve 20 multimodal benchmark test functions, after combining with nondominated sorting and differential evolution. Systematic experiments have indicated that MOMMOP outperforms a number of methods for multimodal optimization, including four recent methods at the 2013 IEEE Congress on Evolutionary Computation, four state-of-the-art single-objective optimization based methods, and two well-known multiobjective optimization based approaches.
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Citations
Bio-inspired computation: Where we stand and what's next
Javier Del Ser,Javier Del Ser,Eneko Osaba,Daniel Molina,Xin-She Yang,Sancho Salcedo-Sanz,David Camacho,Swagatam Das,Ponnuthurai Nagaratnam Suganthan,Carlos A. Coello Coello,Francisco Herrera +10 more
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Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications
TL;DR: This paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of nICHing methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment, and poses challenges and research questions on nichin that are yet to be appropriately addressed.
Adaptive Multimodal Continuous Ant Colony Optimization
TL;DR: An adaptive multimodal continuous ACO algorithm is introduced and an adaptive parameter adjustment is developed, which takes the difference among niches into consideration, which affords a good balance between exploration and exploitation.
A survey on evolutionary computation for complex continuous optimization
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