Hybridization of Soft Computing Techniques for Job Scheduling Problem
Soft Computing Techniques
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This book focused on evolutionary and swarm intelligence technique for scheduling problems and also discussed about genetic algorithm for scheduling, particle swarm optimization and artificial bee colony algorithms and their implementations. In this book, AABC techniques were also discussed.In this adaptive ABC technique, the term adaptiveness is achieved by using mutation, crossover and velocity in the employed bee phase for finding the new food sources. The adaptive ABC algorithm optimally allocates the jobs to the accurate processors or resources. Moreover, these existing techniques mostly ...
This book focused on evolutionary and swarm intelligence technique for scheduling problems and also discussed about genetic algorithm for scheduling, particle swarm optimization and artificial bee colony algorithms and their implementations. In this book, AABC techniques were also discussed.In this adaptive ABC technique, the term adaptiveness is achieved by using mutation, crossover and velocity in the employed bee phase for finding the new food sources. The adaptive ABC algorithm optimally allocates the jobs to the accurate processors or resources. Moreover, these existing techniques mostly concentrate on two factors such as the minimization of the makespan and the completion time. These hybrid techniques have performed well with these two factors and allocate jobs to the resources for increasing the resource utilization. The adaptiveness improves the efficiency of scheduling process when compared to the three conventional hybrid job scheduling techniques.
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