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New Particle Swarm Optimization algorithm for knapsack problem

Ling Ouyang, Dongyun Wang

Open publisher page 4 citations

Abstract

In this paper it proposes an improved Particle Swarm Optimization (PSO) algorithm for the knapsack problem. The new algorithm is based on the standard PSO algorithm for overcoming the shortcomings that standard PSO traps into local optima easily and has a low convergence accuracy. When the load-bearing quantity of the knapsack is exceeded, the fitness will be sit zero. When the best position of the individual particle is the same with the best position of the population, the particle's position will be reinitialized. The simulation shows that the improved algorithm is simple and effective to solve the small-scale knapsack problem.

About this research paper

What this paper is about

In this paper it proposes an improved Particle Swarm Optimization (PSO) algorithm for the knapsack problem. The new algorithm is based on the standard PSO algorithm for overcoming the shortcomings that standard PSO traps into local optima easily and has a low convergence accuracy. When the load-bearing quantity of the knapsack is exceeded, the fitness will be sit zero. When the best position of the individual particle is the same with the best position of the population, the particle's position will be reinitialized. The simulation shows that the improved algorithm is simple and effective to solve the small-scale knapsack problem.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper it proposes an improved Particle Swarm Optimization (PSO) algorithm for the knapsack problem. The new algorithm is based on the standard PSO algorithm for overcoming the shortcomings that standard PSO traps into local optima easily and has a low convergence accuracy. When the load-bearing quantity of the knapsack is exceeded, the fitness will be sit zero. When the best position of the individual particle is the same with the best position of the population, the particle's position will be reinitialized. The simulation shows that the improved algorithm is simple and effective to solve the small-scale knapsack problem.

Key concepts: Knapsack problem, Particle swarm optimization, Mathematical optimization, Position (finance), Convergence (economics), Continuous knapsack problem, Computer science, Algorithm

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