Work

Many-Objective Brain Storm Optimization Algorithm

Year: 2019

Type: article

Abstract: In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving many-objective optimization problems. Inspired by the human brainstorming conference, Brain Storming Optimization (BSO) algorithm was guided by the cluster centers and other individuals with probability, which can balance convergence and diversity greatly. In this paper, the authors propose a novel brain storm optimization algorithm for many-objective optimization problem. The algorithm adopts the decision variable clustering method to divides the variables into convergence-related variables and diversity-related variables. The decomposition strategy is designed to increases selection pressure for the convergence-related variables, while the reference point's strategy is adopted for the diversity-related variables to update the population and increase the diversity. Experimental results show that the proposed many-objective brain storm optimization algorithm is a very promising algorithm for solving many-objective optimization problems.

Source: IEEE Access

DOI: 10.1109/access.2019.2960874

Authors: Yali Wu, Xinrui Wang, Yulong Fu, Guoting Li

Institution: Xi'an University of Technology

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funders: National Youth Foundation of China, National Key Research and Development Program of China