A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems
By Caitong Yue, B. Qu, Jing J. Liang. IEEE Transactions on Evolutionary Computation, 2018
473 citations recorded by the source corpus.
This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value. The proposed method features an index-based ring topology to induce stable niches that allow the identification of a larger number of Pareto-optimal solutions, and adopts a special crowding distance concept as a density metric in the decision and objective spaces. The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces. In addition, new multimodal multiobjective optimization test functions and a novel performance indicator are designed for the purpose of assessing the performance of the proposed algorithms. An effectiveness validation study is carried out comparing the proposed method with five other algorithms using the benchmark functions to prove its effectiveness.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/f65474b8a3c2e9f33112947dd9827a06a972eee6
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Citations in the corpus
- Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, background): “In MO Ring PSO SCD [39], the global best position is the position of the best particle within the particle’s neighborhood determined by a ring topology.”
- Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, background): “Although some MOEAs have solved multimodal multi-objective optimization problems with a few decision variables by preserving the population diversity in the decision space [36, 107, 200], they are difficult to be applied to multimodal LSMOPs.”