Abstract
Recently, the ideal gas molecular movement (IGMM) algorithm was proposed by the authors as a new metaheuristic optimization technique for solving single and multi-objective optimization problems. Ideal gas molecules scatter throughout the confined environment quickly. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized to accomplish the optimal solutions. In this paper a modified IGMM algorithm is proposed based on quantum theory. Quantum based IGMM (QIGMM) is intended for enhancing the ability of the local search and increasing the individual diversity in the population. QIGMM improve capability of IGMM in avoiding the premature convergence and eventually finding the function optimum. startlingly, all these are obtained without introducing additional operators to the basic IGMM algorithm. The effectiveness of these improvements is tested by some standard benchmark optimization problems. experimental results show that, QIGMM algorithm is more effective and efficient than the original IGMM and other approaches.
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Ghasemi, M.R., Varaee, H. (2018). Modified Ideal Gas Molecular Movement Algorithm Based on Quantum Behavior. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-67988-4_148
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DOI: https://doi.org/10.1007/978-3-319-67988-4_148
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