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A modified Intellects-Masses Optimizer for solving real-world optimization problems
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DOI：10.1016/j.swevo.2018.02.015.png)
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Abstract
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The Intellects-Masses Optimizer (IMO) is a recently-proposed cultural algorithm, which is easy to understand, use, and implement. IMO requires (almost) no parameter tuning and has successfully been used to tackle unconstrained continuous optimization problems. A modified variant of IMO, called MIMO, is proposed in this paper. The proposed method uses improved update equations, a self-adaptive scaling factor, duplicates removal, and a local search to improve the performance of IMO. The MIMO method is tested on the 22 IEEE CEC 2011 real-world benchmark problems and is compared with 14 state-of-the-art algorithms. The results demonstrate the out-performance of the proposed method and its superiority compared to the original IMO algorithm.
Keywords:
Cultural algorithms
Intellects-Masses Optimizer
Metaheuristics
Stochastic search
Real-world optimization
Continuous optimization
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