Analysis of Solution Quality of a Multiobjective Optimization-based Evolutionary Algorithm for Knapsack Problem

TL;DR

This paper provides a theoretical analysis of a multi-objective evolutionary algorithm for the 0-1 knapsack problem, focusing on solution quality and approximation ratios with different initialisation methods.

Contribution

It introduces a theoretical framework for evaluating the solution quality of a multi-objective evolutionary algorithm applied to the knapsack problem, considering initialisation strategies.

Findings

Analysis of approximation ratios for different initialisation methods

Comparison of local search and greedy search initialisations

Insights into solution quality in multi-objective evolutionary algorithms

Abstract

Multi-objective optimisation is regarded as one of the most promising ways for dealing with constrained optimisation problems in evolutionary optimisation. This paper presents a theoretical investigation of a multi-objective optimisation evolutionary algorithm for solving the 0-1 knapsack problem. Two initialisation methods are considered in the algorithm: local search initialisation and greedy search initialisation. Then the solution quality of the algorithm is analysed in terms of the approximation ratio.