Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

Yanyan Tan,Xue Lu,Qiang Wang,Huaxiang Zhang,Yan Liu

doi:10.1155/2019/6943921

Abstract

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

Highlights

Multiobjective optimization problems (MOPs) widely exist in applications [1], such as design [2], scheduling [3,4,5], path planning [6], retrieval [7], and cloud computing [8]
In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization
Based on the smart and distinct features of IWO and multiobjective evolutionary algorithms (MOEAs)/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm

Summary

Introduction

Multiobjective optimization problems (MOPs) widely exist in applications [1], such as design [2], scheduling [3,4,5], path planning [6], retrieval [7], and cloud computing [8]. Kundu et al [33] proposed multiobjective invasive weed optimization (IWO) in 2011 and applied it on solving CEC 2009 MOPs. In their work, fuzzy dominance mechanism, instead of nondominated sorting, was carried out to sort the promising weeds in each iteration. Based on the smart and distinct features of IWO and MOEA/D, we propose multiobjective invasive weed optimization algorithm based on decomposition (MOEA/DIWO) and try to combine their excellent features in this extended hybrid algorithm. The performance of the proposed MOEA/D-IWO in both convergence speed and optimality of results are compared with those of NSGA-II, MOEA/D, and some other multiobjective evolutionary algorithms on a big set of MOPs. Comparison results indicate the feasibility of IWO as a very hopeful metaheuristic candidate in the domain of multiobjective optimization.

Background

Multiobjective Invasive Weed Optimization Algorithm Based on Decomposition

Experiments

Experimental Analysis

Conclusion