Article: Solving many-objective optimisation problems by an improved particle swarm optimisation approach and a normalised penalty method Journal: International Journal of Bio-Inspired Computation (IJBIC) 2019 Vol.14 No.4 pp.247 - 264 Abstract: In this paper, a novel modified particle swarm optimisation (NMPSO) approach is presented to handle the many-objective knapsack (MOK) problem. NMPSO relies on the global best particle to guide the search of all particles in each generation. Furthermore, a randomisation-based mutation is adopted to overcome the premature convergence. A normalised penalty method (NPM) is devised to reach a compromise between objective functions and inequality constraints, which enables particles to explore solution space more precisely. In summary, the contribution of our work can be summarised in two aspects: 1) a more powerful approach called NMPSO is proposed; 2) a reasonable NPM is devised. Five improved PSOs are used to handle the MOKs with different number of objective functions and dimensions. Experimental results show that NMPSO has higher efficiency than the other four approaches. It uses the lowest computational cost and achieves the smallest penalty function values for most MOKs. Inderscience Publishers - linking academia, business and industry through research

Title: Solving many-objective optimisation problems by an improved particle swarm optimisation approach and a normalised penalty method

Authors: Dexuan Zou; Fei Wang; Nannan Yu; Xiangyong Kong

Addresses: School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu, China ' School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu, China ' School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu, China ' School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu, China

Abstract: In this paper, a novel modified particle swarm optimisation (NMPSO) approach is presented to handle the many-objective knapsack (MOK) problem. NMPSO relies on the global best particle to guide the search of all particles in each generation. Furthermore, a randomisation-based mutation is adopted to overcome the premature convergence. A normalised penalty method (NPM) is devised to reach a compromise between objective functions and inequality constraints, which enables particles to explore solution space more precisely. In summary, the contribution of our work can be summarised in two aspects: 1) a more powerful approach called NMPSO is proposed; 2) a reasonable NPM is devised. Five improved PSOs are used to handle the MOKs with different number of objective functions and dimensions. Experimental results show that NMPSO has higher efficiency than the other four approaches. It uses the lowest computational cost and achieves the smallest penalty function values for most MOKs.

Keywords: many-objective optimisation; MOP; novel modified particle swarm optimisation; NMPSO; many-objective knapsack; randomisation-based mutation; normalised penalty method; NPM.

DOI: 10.1504/IJBIC.2019.103954

International Journal of Bio-Inspired Computation, 2019 Vol.14 No.4, pp.247 - 264

Received: 23 Mar 2017
Accepted: 25 Sep 2017
Published online: 03 Dec 2019 *

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Title: Solving many-objective optimisation problems by an improved particle swarm optimisation approach and a normalised penalty method

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