Improved Particle Swarm Optimization for Solving Multiprocessor Scheduling Problem: Enhancements and Hybrid Methods
نویسنده
چکیده
Memetic algorithms (MAs) are hybrid evolutionary algorithms (EAs) that combine global and local search by using an EA to perform exploration while the local search method performs exploitation. Combining global and local search is a strategy used by many successful global optimization approaches, and MAs have in fact been recognized as a powerful algorithmic paradigm for evolutionary computing. This paper presents a hybrid heuristic model that combines particle swarm optimization (PSO) and simulated annealing (SA). This PSO/SA hybrid was applied on the multiprocessor scheduling problem to perform static allocation of tasks in a heterogeneous distributed computing system in a manner that is designed to minimize the cost. Additionally, this paper also focuses on the design and implementation of several enhancements to PSO based on diversity and efficient initialization using different distributions. The results show the effectiveness and superiority of the hybrid algorithms. Key-Words: Memetic Algorithms, Particle Swarm Optimization, Simulated Annealing, Hybrid, Multiprocessor Scheduling, Optimization
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