Abstract
Unlike static optimization problems, the position, height and width of the peaks may vary with time instances in dynamic optimization problems (DOPs). Many real world problems are dynamic in nature. Evolutionary Algorithms (EAs) have been considered to solve the DOPs in the recent years. This article proposes a multi-population based Differential Evolution algorithm which uses a local mutation to control the perturbation of individuals and also avoid premature convergence. An exclusion rule is used to maintain the diversity in a subpopulation to cover a larger search space. Speciation-based memory archive has been used to utilize the previously found optimal information in the new change instance. Furthermore the proposed algorithm has been compared with four other state-of-the-art EAs over the Moving Peak Benchmark (MPB) problem and a benchmarks set named Generalized Dynamic Benchmark Generator (GDBG) proposed for the 2009 IEEE Congress on Evolutionary Computation (CEC) competition.
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Kundu, S., Basu, D., Chaudhuri, S.S. (2013). Multipopulation-Based Differential Evolution with Speciation-Based Response to Dynamic Environments. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_21
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DOI: https://doi.org/10.1007/978-3-319-03753-0_21
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