Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization.

Wang Chen,Zhao Guohua,Seyedali Mirjalili

doi:10.1371/journal.pone.0240131

Wang Chen, Zhao Guohua + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0240131

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Journal: PloS one	Publication Date: Oct 9, 2020
License type: CC BY 4.0

Affiliation: Hubei University of Automotive Technology

Abstract

In the EMO (evolutionary multi-objective, EMO) algorithm, MaOPs (many objective optimization problems, MaOPs) are sometimes difficult to keep the balance of convergence and diversity. The decomposition based EMO developed for MaOPs has been proved to be effective, and BBO/Complex (the biogeography based optimization for complex system, BBO/Complex) algorithm is a low complexity algorithm. In this paper, a decomposition and adaptive weight adjustment based BBO/Complex algorithm (DAWA-BBO/Complex) for MaOPs is proposed. First, a new method based on crowding distance is designed to generate a set of weight vectors with good uniformly. Second, an adaptive weight adjustment method is used to solve MaOPs with complex Pareto optimal front. Subsystem space obtains a non-dominated solution by a new selection strategy. The experimental results show that the algorithm is superior to other new algorithms in terms of convergence and diversity in DTLZ benchmark problems. Finally, the algorithm is used to solve the problem of NC (numerical control machine, NC) cutting parameters, and the final optimization result is obtained by AHP (Analytic Hierarchy Process, AHP) method. The results show that the cutting speed is 10.8m/min, back cutting depth is 0.13mm, the cutting time is 504s and the cutting cost is 22.15yuan. The proposed algorithm can effectively solve the practical optimization problem.

Highlights

In scientific research and production practice, Multi-objective optimization problems (MOPs) is of great importance [1]
multi-objective evolutionary algorithms (MOEA)/D-PBI [13] is the representative of the decomposition based method, which uses a series of predefined weight vectors to keep the diversity of solutions
A novel many-objective optimization algorithm called DAWA-Biogeography based optimization (BBO)/Complex is proposed by this paper

Summary

Introduction

In scientific research and production practice, MOPs (multi-objective optimization, MOPs) is of great importance [1]. MOP, and there is little research on MaOPs. In the previous research [10], the paper designed the BBO/complex algorithm framework based on decomposition to solve MaOPs. The basic assumption of MOEA/D employs a predefined set of uniformly distributed weight vectors. The paper designed the adaptive weight vector adjustment to ensure a certain number of subsystems and increase the diversity of the algorithm to achieve the optimal solution effect. The algorithm is used to solve the MaOPs. With the further research, the value of weight vector will greatly affect the search efficiency and diversity of the solution when the complex system is divided into several subsystems based on decomposition strategy. The paper have developed a new framework of DAWA-BBO/Complex algorithm for MaOPs, and a new method based on crowding distance is designed to generate a set of weight vectors with good uniformly.

Basic definitions

1: Initialization all the parameters

Experimental results and discussion

Significance test

Conclusions