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Gompertz pso variants for Knapsack and Multi-Knapsack Problems

Applied Mathematics(A Journal of Chinese Universities) 2021年第4期36卷 611-630页

作者： Pinkey Chauhan Millie Pant Kusum Deep Department of Mathematics Jaypee Institute of Information TechnologyNoida 201304India

Particle Swarm Optimization,a potential swarm intelligence heuristic,has been recognized as a global optimizer for solving various continuous as well as discrete optimization *** by the performance of Gompertz pso on a set of continuous problems,this works extends the application of Gompertz pso for solving binary optimization ***,a new chaotic variant of Gompertz pso namely Chaotic Gompertz binary Particle Swarm Optimization(CGBpso)has also been *** new variant is further analysed for solving binary optimization *** new chaotic variant embeds the notion of chaos into GBpso in later stages of searching process to avoid stagnation *** efficiency of both the binary pso variants has been tested on different sets of Knapsack Problems(KPs):0-1 Knapsack Problem(0-1 KP)and Multidimensional Knapsack Problems(MKP).The concluding remarks have made on the basis of detailed analysis of results,which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using Bpso,GBpso and CGBpso.

关键词： binary pso Knapsack Problems Multi Knapsack Problems Gompertz function chaos

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A Novel Multi-Mutation binary Particle Swarm Optimization for 0/1 knapsack problem

A Novel Multi-Mutation Binary Particle Swarm Optimization fo...

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2009中国控制与决策会议

作者： Zhuangkuo Li,Ning Li School of management,Guilin University of Electronic Technology,Guilin 541004,China

＜正＞Particle swarm optimization（pso） algorithm as a novel computational intelligence technique has been applied successfully in many continuous optimization *** the binary pso（Bpso） is developed and Qi presented a modified binary pso（QBpso）.As the two algorithms have not a satisfactory optimization capability,here in order to tackle the 0/1 knapsack problem effectively,Multi-Mutation strategy including single mutation operator and full mutation operator binary particle swarm optimization（MMBpso） is proposed based QBpso *** mutation operator can be considered as a microcosmic mutation,which adjusts the particle in local bit with a random *** mutation operator is a macroscopic mutation that may change particle’s all bits by a random probability. In optimization process,MMBpso allows the generation of infeasible solutions,and uses two methods called greedy transform algorithm and penalty function method to produce the best outcomes for constraint handling,*** simulation results for a series of benchmark 0/1 knapsack problems show that the proposed MMBpso outperforms the traditional binary pso algorithm and QBpso,especially with the increasing quantity of the goods,as MMBpso can effectively escape from the local optima to avoid premature convergence and obtain better solutions.

关键词： binary pso Multi-Mutation Greedy transform algorithm and penalty function method 0/1 Knapsack Problem

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