NDLI: A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

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A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

Content Provider	Semantic Scholar
Author	Wang, Xiaojuan Gao, Liang Zhang, Chaoyong Shao, Xinyu
Copyright Year	2009
Abstract	Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multiobjective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://115.156.244.89/~gaoliang/IJAMT%20WangXJ%20final.pdf
Alternate Webpage(s)	http://202.114.11.143/~gaoliang/IJAMT%20WANG%20XIAOJUAN%20ONLINE.pdf
Language	English
Access Restriction	Open
Subject Keyword	Ant colony optimization algorithms Archive Attention deficit hyperactivity disorder Chromosome Structures Concentrate Dosage Form Evolutionary algorithm Experiment Genetic algorithm Heuristic Heuristics Job Syndrome Job shop scheduling Mathematical optimization Multi-objective optimization Mutation Numerical analysis Numerous Paper Pareto efficiency Particle swarm optimization Polynomial-time approximation scheme PowerA Premature convergence Scheduling (computing) Scheduling - HL7 Publishing Domain
Content Type	Text
Resource Type	Article

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