Journal ArticleDOI
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems
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TLDR
A coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem and is compared to several state-of-the-art algorithms tailored for CMOPs.Abstract:
Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions To remedy this issue, this article proposes a coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one While the two populations are evolved by the same optimizer separately, the assistance in solving the original CMOP is achieved by sharing useful information between the two populations In the experiments, the proposed framework is compared to several state-of-the-art algorithms tailored for CMOPs High competitiveness of the proposed framework is demonstrated by applying it to 47 benchmark CMOPs and the vehicle routing problem with time windowsread more
Citations
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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 Dual-Population-Based Evolutionary Algorithm for Constrained Multiobjective Optimization
TL;DR: Comparison against six state-of-the-art CMOEAs demonstrates that c-DPEA is significantly superior or comparable to the contender algorithms on most of the test problems.
Journal ArticleDOI
Learning to Optimize: Reference Vector Reinforcement Learning Adaption to Constrained Many-Objective Optimization of Industrial Copper Burdening System
TL;DR: In this article , an adaptive reference vector reinforcement learning (RVRL) approach was proposed to decomposition-based algorithms for industrial copper burdening optimization, where the RL operation treated the reference vector adaptation process as an RL task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics.
Journal ArticleDOI
A Benchmark-Suite of real-World constrained multi-objective optimization problems and some baseline results
Abhishek Kumar,Guohua Wu,Mostafa Z. Ali,Qizhang Luo,Rammohan Mallipeddi,Ponnuthurai Nagaratnam Suganthan,Swagatam Das +6 more
TL;DR: This work develops a benchmark suite of Real-world Constrained Multi-objective Optimization Problems (RWCMOPs) for performance assessment of CMOMs and presents the baseline results by using state-of-the-art algorithms.
Journal ArticleDOI
Dynamic Selection Preference-Assisted Constrained Multiobjective Differential Evolution
TL;DR: A dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm that exhibits superior or at least competitive performance, in comparison with other well-established methods.
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.
Journal ArticleDOI
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
Journal ArticleDOI
Algorithms for the vehicle routing and scheduling problems with time window constraints
TL;DR: This paper considers the design and analysis of algorithms for vehicle routing and scheduling problems with time window constraints and finds that several heuristics performed well in different problem environments; in particular an insertion-type heuristic consistently gave very good results.
Journal Article
Simulated Binary Crossover for Continuous Search Space.
TL;DR: A real-coded crossover operator is developed whose search power is similar to that of the single-point crossover used in binary-coded GAs, and SBX is found to be particularly useful in problems having mult ip le optimal solutions with a narrow global basin where the lower and upper bo unds of the global optimum are not known a priori.
Journal ArticleDOI
Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II
Hui Li,Qingfu Zhang +1 more
TL;DR: The experimental results indicate that MOEA/D could significantly outperform NSGA-II on these test instances, and suggests that decomposition based multiobjective evolutionary algorithms are very promising in dealing with complicated PS shapes.
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