Optimizing Particle Swarm Optimization to Solve Knapsack Problem

Abstract

Abstract. Knapsack problem, a typical problem of combinatorial optimization in operational research, has broad applied foregrounds. This paper applies particle swarm optimization to solve discrete 0/1 knapsack problem. However, traditional particle swarm optimization has nonnegligible disadvantages: all the parameters in the formula affect the abilities of local searching and global searching greatly, which is liable to converge too early and fall into the situation of local optimum. This paper modifies traditional particle swarm optimization, and makes the position of particle which achieves global optimization reinitializated. Through analyzing the final result, the paper has proven that the improved algorithm could improve searching ability of particle swarm, avoid converging too early and solve 0/1 knapsack problem more effectively.

An open Jackson Network Model for Heterogeneous Infrastructure as a Service on Cloud Computing

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“…Hybrid Algorithm has been proposed which is a combination of Genetic and Particle Swarm Optimization Technique [8]. GA suffers from the drawback that it traps in local optima, that are it does not know how to sacrifice short term fitness for long term fitness.…”

Projects Selection In Knapsack Problem By Using Artificial Bee Colony Algorithm

“…Many methods such as: branch and bound, cutting planes, and dynamic programming etc., and nature inspired algorithm have been proposed to solve the knapsack problem [3,4]. Lately, evolutionary algorithms such as genetic algorithm (GA) [5,6], particle swarm optimization algorithm (PSO) [7], ant colony optimization algorithm (ACO) [8] and artificial bee colony algorithm (ABC) [4] efficiently have been developed for solving knapsack problem and reaches to the best solution. In this paper uses ABC algorithm which proved efficiently in select subset of projects in knapsack problem to build best investment plan compared by GA.…”