A novel constraint multi-objective artificial physics optimisation algorithm and its convergence. (1st January 2011)

• Record Type: Journal Article
• Authors: Wang, Yan; Zeng, Jian-Chao; Cui, Zhi-Hua; He, Xiao-Juan
• Is Part Of: International journal of innovative computing and applications. Volume 3:Number 2(2011)
• Page Start: 61
• Page End: 70
• Publication Date: 2011-01-01
• Subjects: constraint -- multi-objective -- artificial physics optimisation -- convergence -- force decreasing; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• DOI: 10.1504/IJICA.2011.039589
• Languages: English
• ISSNs: 1751-648X

Abstract

This paper presents a constraint multi-objective artificial physics optimisation (CMOAPO) algorithm by introducing a novel optimisation paradigm called artificial physics optimisation (APO) into constraint multi-objective domain. Combining with characteristics of constraint multi-objective optimisation problems, a method of virtual force decreasing is incorporated into CMOAPO to decrease the probability of individuals moving from feasible region into infeasible region. Furthermore, the convergence of CMOAPO is analysed in terms of theory with related knowledge of probability. The performance of CMOAPO algorithm is tested using several benchmark functions. The results obtained show that the proposed approach is effective.