Work

A proportion-based selection scheme for multi-objective optimization

Year: 2017

Type: conference-paper

Abstract: Classical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multi-objective optimizations problems when the number of objectives increases due to the lack of sufficient selection pressure towards the Pareto front (PF). This poses a great challenge to the design of MOEAs. To cope with this problem, researchers have developed reference-point based methods, where some well-distributed points are produced to assist in maintaining good diversity in the optimization process. However, the convergence speed of the population may be severely affected during the searching procedure. This paper proposes a proportion-based selection scheme (denoted as PSS) to strengthen the convergence to the PF as well as maintain a good diversity of the population. Computational experiments have demonstrated that PSS is significantly better than three peer MOEAs on most test problems in terms of diversity and convergence.

Source: IEEE Symposium Series on Computational Intelligence (SSCI)

DOI: 10.1109/ssci.2017.8285266

Authors: Liuwei Fu, Juan Zou, Shengxiang Yang, Gan Ruan, Zhongwei Ma, Jinhua Zheng

Institutions: Xiangtan University, De Montfort University, Central South University

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Engineering and Physical Sciences Research Council

Awards: Evolutionary Computation for Dynamic Optimisation in Network Environments, G5466298806, G7042882887