A parallel algorithm of simulated annealing for multiprocessor scheduling

A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system

“…Many famous metaheuristic, such as GA [19,30,39], AIS [31,44,46], ACO [5], PSO [34], SA [26], TS [36], and VNS [10] have been successfully applied to a broad range of scheduling problems. Nevertheless, to trade off better performance in terms of schedule quality, non-deterministic algorithms always have much higher computational cost.…”

“…In attempts to obtain schedules of better quality, many well-known metaheuristics, including genetic algorithm (GA) [1,6,8,21,33,37,42], artificial immune system (AIS) [44], ant colony optimization (ACO) [5], particle swarm optimization (PSO) [34], simulated annealing (SA) [26], tabu search (TS) [36], and variable neighborhood search (VNS) [32], have been adopted. Generally, metaheuristics can be divided into trajectory methods (also named local search heuristics) and population-based methods.…”

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“…Task duplication heuristic algorithms allocate duplication of some tasks on more than one processor, redundantly, thereby the interprocessor communication cost will be reduced [16,17]. The second category includes population-based algorithms such as PSO, Genetic, AIS [17][18][19][20] and also includes trajectory algorithms like Variable Neighbourhood Search (VNS), simulated annealing algorithms [21,22]. These methods find the solution in longer time in comparison to the above mentioned methods.…”

A Bi- Objective Workflow Application Scheduling In Cloud Computing Systems

“…Since a good scheduling method would impress on the system performance and there is no direct method to find an optimal solution in polynomial time, the scheduling process must rely on finding the best solution within possibilities. A number of meta-heuristic based methods were presented to solve NP problems such as: particle swarm optimization (PSO) [5], tabu search (TS) [6], simulated annealing (SA) [7], genetic algorithm (GA) etc. In contrast, GA by [8] [9] [10] are known to give good results in several…”

“…A number of meta-heuristic based methods were presented to solve NP problems such as: particle swarm optimization (PSO) [5], tabu search (TS) [6], simulated annealing (SA) [7], genetic algorithm (GA) etc. In contrast, GA by [8] [9] [10] are known to give good results in several optimization domains and provide robust search techniques that allow a high-quality solution to be obtained from a large search space and parallel search in polynomial time by applying the principle of evolution.…”