Mina Moosapour; Ahmad Bagheri; Mohammad Javad Mahmoodabadi
Abstract
The imperialist competitive algorithm (ICA) is developed based on the socio-political process of imperialist competitions. It is an efficient approach for single-objective optimization problems. However, this algorithm fails to optimize multi-objective problems (MPOs) with conflicting objectives. This ...
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The imperialist competitive algorithm (ICA) is developed based on the socio-political process of imperialist competitions. It is an efficient approach for single-objective optimization problems. However, this algorithm fails to optimize multi-objective problems (MPOs) with conflicting objectives. This paper presents a modification of the ICA to different multi-objective problems. To improve the algorithm performance and adapt to the characteristics of MOPs, the Sigma method was used to establish the initial empires, the weighted sum approach (WSum) was employed for empire competition, and an adaptive elimination approach was used for external archiving strategy. the results indicated that the suggested algorithm had a higher performance compared to other algorithms based on diversity and convergence characteristics.
Aboozar Zandvakili; Najme Mansouri; Mohammad Masoud Javidi
Abstract
Task scheduling in cloud computing plays an essential role for service provider to enhance its quality of service. Grasshopper Optimization Algorithm (GOA) is an evolutionary computation technique developed by emulating the swarming behavior of grasshoppers while searching for food. GOA is easy to implement ...
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Task scheduling in cloud computing plays an essential role for service provider to enhance its quality of service. Grasshopper Optimization Algorithm (GOA) is an evolutionary computation technique developed by emulating the swarming behavior of grasshoppers while searching for food. GOA is easy to implement but it cannot make full utilization of every iteration, and there is a risk of falling into the local optimal. This paper proposes a suitable approach for adjusting the comfort zone parameter based on the fuzzy signatures called signature GOA (SGOA) to balance exploration and exploitation. Then, we propose task scheduling based on SGOA by considering different objectives such as completion time, delay, and the load balancing on the machines. Finally, different algorithms such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), Tabu Search (TS), and multi-objective genetic algorithm, are used for comparison. The results show that among all algorithms, SGOA can be successful in much less iteration.
