Niching methods for genetic algorithms
Niching methods for genetic algorithms
Computers and Industrial Engineering
Using evolutionary computation and local search to solve multi-objective flexible job shop problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Computers and Industrial Engineering
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A new hybrid GA/SA algorithm for the job shop scheduling problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
A survey on B*-Tree-based evolutionary algorithms for VLSI floorplanning optimisation
International Journal of Computer Applications in Technology
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In many real-world applications, processing times may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. 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 genetic algorithm based on immune and entropy principle to solve the multi-objective fuzzy FJSP. In this improved multi-objective algorithm, 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 computational results demonstrate the effectiveness of the proposed algorithm.