Journal Article10.1051/RO/2017014
A new hypervolume-based differential evolution algorithm for many-objective optimization
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TL;DR: In MODEhv, a modified differential evolution paradigm with automatic parameter configuration strategy is used to balance exploration and exploitation of the algorithm, and the hypervolume indicator is incorporated for the selection of solutions to be varied and Solutions to be kept in archive respectively.
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Abstract: Evolutionary algorithms are successfully used for many-objective optimization. However, solutions are prone to become nondominated from each other with the increase in the number of objectives, which reduces the efficiency of Pareto dominance-based algorithms. In this paper, a new hypervolume-based differential evolution algorithm (MODEhv) is proposed for many-objective optimization problems (MaOPs). In MODEhv, a modified differential evolution paradigm with automatic parameter configuration strategy is used to balance exploration and exploitation of the algorithm. Besides, the hypervolume indicator is incorporated for the selection of solutions to be varied and solutions to be kept in archive respectively. Finally, a threshold technique is employed to improve diversity of solutions in archive. MODEhv is investigated on a set of widely used benchmark problems and compared with five state-of-the-art algorithms. The experimental results show the efficiency of MODEhv for solving MaOPs.
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Citations
Adaptive Sorting-Based Evolutionary Algorithm for Many-Objective Optimization
TL;DR: An adaptive sorting-based evolutionary algorithm based on the idea of decomposition that provides an adaptive promising subpopulation sorting- based environmental selection strategy for problems which may have irregular Pareto fronts.
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Review of the Research Landscape of Multi-Criteria Evaluation and Benchmarking Processes for Many-Objective Optimization Methods: Coherent Taxonomy, Challenges and Recommended Solution
R. T. Mohammed,Razali Yaakob,A. A. Zaidan,Nurfadhlina Mohd Sharef,R. H. Abdullah,B. B. Zaidan,Kareem Abbas Dawood +6 more
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A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization
TL;DR: In this article , a projection-based evolutionary algorithm, MOEA/PII, is proposed to divide the objective space into projection plane and free dimension(s) for many-objective optimization problems.
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Exploring Mutation Strategy of Evolution Algorithm for Global Optimization Problem
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TL;DR: The proposed mutation strategy enhances the performance of DE in global optimization by balancing exploration and exploitation, improving convergence speed and solution quality.
References
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.
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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