Hybrid Model for Solving Multi-Objective Problems Using Evolutionary Algorithm and Tabu Search

TL;DR

This paper introduces a hybrid optimization model combining tabu search and evolutionary algorithms to effectively solve multi-objective problems, demonstrating its performance on benchmark functions using distributed computing.

Contribution

The paper proposes a novel hybrid model that leverages the strengths of tabu search and evolutionary algorithms for multi-objective optimization.

Findings

Effective on benchmark functions ZDT1, ZDT2, ZDT3

Utilized distributed computing for testing

Shows improved exploration and neighborhood search capabilities

Abstract

This paper presents a new multi-objective hybrid model that makes cooperation between the strength of research of neighborhood methods presented by the tabu search (TS) and the important exploration capacity of evolutionary algorithm. This model was implemented and tested in benchmark functions (ZDT1, ZDT2, and ZDT3), using a network of computers.