2015/02/12 by Jun He, Yong Wang, He, Jun +3
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Packing Problems #cs.NE
paper · pdf · doi:10.48550/arxiv.1502.03699
arxiv created 2015/02/12 · openalex publication_date 2015/02/12 · arxiv updated 2015/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.