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Memetic algorithm (MA) often perform better than other evolutionary algorithm due to their combining the local search with the process of global optimization. However, like any other evolutionary algorithm (EA), MA due to the problem of genetic drift often result in sub-optimal solutions. The problem is more aggravated when EAs are applied to search complex landscape of NP complete problem like protein...
Global optimization process can often be divided into two subprocesses: exploration and exploitation. The tradeoff between exploration and exploitation (T:Er&Ei) is crucial in search and optimization, having a great effect on global optimization performance, e.g., accuracy and convergence speed of optimization algorithms. In this paper, definitions of exploration and exploitation are first given...
The 0-1 knapsack problem (KP) is one of the classical NP-hard problems with binary decision variables. The traditional differential evolution (DE) is an effective stochastic parallel search evolutionary algorithm for global optimization based on real valued crossover and mutation operations in continuous space. To solve KPs, based on DE, a discrete binary version of differential evolution (DBDE) was...
The method of multiple heterogeneous ant colonies with information exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity. The proposed method which is inspired by biology and psychology has a structure composed of several ant colonies. These colonies participate in solving problems in a concurrently manner...
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