Abstract
Recently a new optimization algorithm, Environmental Adaption Method (EAM) has been proposed to solve optimization problems.EAM target its search toward optimal solution using two operators adaption and mutation operator. Both of these operators perform random search of full search space until they got a good solution. Although EAM has a good convergence rate yet it can be further improved if instead of performing random search of overall search space, operators limit their search to a finite region that has a very high probability containing optimal solution. Proposed algorithm select this region by utilizing the information received from the known genomic structures of best solutions obtained in previous generations. A very similar idea was used in Particle Swarm Optimization algorithm however unlike PSO it does not require additional store. Updated version is very fast as compared to basic EAM algorithm. Different state of art algorithms are compared on benchmark functions to check its performance.
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Mishra, K.K., Tiwari, S., Misra, A.K. (2012). Improved Environmental Adaption Method for Solving Optimization Problems. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_34
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DOI: https://doi.org/10.1007/978-3-642-34289-9_34
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