Journal ArticleDOI
A Multimodal Multiobjective Evolutionary Algorithm Using Two-Archive and Recombination Strategies
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TLDR
A novel multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies to solve multi-objective optimization problems and the overall performance of the proposed algorithm is significantly superior to the competing algorithms.Abstract:
There have been few researches on solving multimodal multiobjective optimization problems, whereas they are commonly seen in real-world applications but difficult for the existing evolutionary optimizers. In this paper, we propose a novel multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. In the proposed algorithm, the properties of decision variables and the relationships among them are analyzed at first to guide the evolutionary search. Then, a general framework using two archives, i.e., the convergence and the diversity archives, is adopted to cooperatively solve these problems. Moreover, the diversity archive simultaneously employs a clustering strategy to guarantee diversity in the objective space and a niche-based clearing strategy to promote the same in the decision space. At the end of evolution process, solutions in the convergence and the diversity archives are recombined to obtain a large number of multiple Pareto optimal solutions. In addition, a set of benchmark test functions and a performance metric are designed for multimodal multiobjective optimization. The proposed algorithm is empirically compared with two state-of-the-art evolutionary algorithms on these test functions. The comparative results demonstrate that the overall performance of the proposed algorithm is significantly superior to the competing algorithms.read more
Citations
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Journal ArticleDOI
A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts
TL;DR: A comprehensive survey of the research on MOPs with irregular Pareto fronts can be found in this article, where a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed.
Journal ArticleDOI
A novel scalable test problem suite for multimodal multiobjective optimization
TL;DR: A landscape visualization method for multiobjective problems is proposed to show the properties of the multimodal multi objective test problems, which have various properties, such as presence of local Pareto optimal set, scalable number of PS, nonuniformly distributed PSs, discrete Pare to front (PF), and scalableNumber of variables and objectives.
Journal ArticleDOI
Evolutionary Large-Scale Multi-Objective Optimization: A Survey
TL;DR: A comprehensive survey of MOEAs for solving large-scale multi-objective optimization problems is presented in this article, where a categorization of MOEA into decision variable grouping based, decision space reduction based, and novel search strategy based MOEA is discussed.
Journal ArticleDOI
A modified particle swarm optimization for multimodal multi-objective optimization
XuWei Zhang,Hao Liu,LiangPing Tu +2 more
TL;DR: A dynamic neighborhood-based learning strategy is introduced to replace the global learning strategy, which enhances the diversity of the population and has competitive performance compared with 5 state-of-the-art multimodal multi-objective algorithms.
Journal ArticleDOI
A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems
TL;DR: The experimental results suggest that the proposed SS-MOPSO algorithm is able to solve the multimodal multi-objective problems effectively and shows superior performance by finding more and better distributed Pareto solutions.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Journal ArticleDOI
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
SPEA2: Improving the strength pareto evolutionary algorithm
TL;DR: An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method.
Journal ArticleDOI
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
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