Two-Stage Adaptive Constrained Particle Swarm Optimization Based on Bi-Objective Method

Qian Feng,Heng Wang,Qing Li,Yongfeng Feng,Yichen Pan

doi:10.1109/access.2020.3013886

Abstract

For the sake of better balancing the relationship between diversity and convergence when handling constrained optimization problems, a two-stage adaptive constrained particle swarm optimization algorithm based on bi-objective method (TABC-PSO) is proposed. In accordance with different phases of the constraint process, the target-constraint space derived from the angle is partitioned adaptively, and simultaneously the global best particle is selected and the external archive set is safeguarded. In the first stage, the whole space is divided adaptively in term of the angular distribution of individual, and the feasible region is explored comprehensively. In the second stage, local regions are adaptively compartmentalized and in-depth exploitation is carried out. Primary and secondary external archive sets are established to maintain population diversity and accelerate convergence. The two phases are switched adaptively in light of the storage status of the two external archive sets. We evaluated TABC-PSO algorithm on the benchmark functions in CEC 2006 and CEC 2017. The experimental results show that TABC-PSO algorithm compared with other state-of-the-art algorithms can be superior to applied to test functions with different types of constraints and possesses a competitive search capability.

Highlights

Optimization problems have been used more and more widely in many fields such as scientific research, industrial production, engineering technology, and economic management
The results of six algorithms improved random sorting method (ISR), simple multi-agent evolution strategy (SMES), ATMES and particle swarm optimization (PSO)+, AHPSOMO and CVI-PSO are directly extracted from their original papers
The comparison between TABC-PSO and other six algorithms shows that, except for the test functions g02 and g09, TABC-PSO algorithm is superior to all the comparison algorithms or is equal to the comparison algorithm in most cases

Summary

INTRODUCTION

Optimization problems have been used more and more widely in many fields such as scientific research, industrial production, engineering technology, and economic management. As a typical evolutionary algorithm, particle swarm optimization (PSO) shows good rapidity and convergence when dealing with COPs. With particle swarm algorithm as search mechanism, penalty function method based on fuzzy rules [25], penalty function with memory [26], adaptive penalty function method [27] has been applied for constraint optimization. In addition to the widespread penalty function method, PSO algorithm is combined with other constraint processing methods, and has achieved good optimization results. The bi-objective method is applied to constraint optimization in combination with adaptive angle region division and the establishment of the two external archive sets in the entire target-constraint (f -v) two-dimensional space. Fmin and fmax are the minimum and maximum values of the objective function of all individuals in the current population, respectively

3) INTRODUCTION OF PARTICLE SWARM ALGORITHM

GBEST SELECTION MECHANISM

CONCLUSION AND FUTURE WORK