Modified Differential Evolution with Locality induced Genetic Operators for dynamic optimization

The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems

“…Simões and Costa [8] investigated a GA based on an immune system for DOPs. In addition, the differential evolution (DE) has been applied to solve the DOPs as can be found in Mendes and Mohais [9], Brest et al [10], and Mukherjee et al [11]. Mendes and Mohais [9] introduced a multipopulation DE for the DOPs.…”

“…Brest et al [10] proposed a self-adaptive multipopulation DE integrated with aging mechanism for DOPs. Mukherjee et al [11] presented a variant of DE that modified the genetic operators of the DE to suit the dynamic optimization. The proposed algorithm utilized locality-induced mutation and crossover operators that acted as a retention strategy by employing the previously stored information to adaptively deal with the DOPs.…”

“…Li and Yang [38] proposed a variant of PSO (CPSO) that employed a hierarchical clustering method and a fast local search to address the dynamic environments constructed by the benchmarks. Mukherjee et al [11] presented a modified variant of DE (MDE-LiGO) that utilized locality-induced genetic operators and tested it on the same benchmarks. Good surveys regarding the problems and metaheuristic algorithms in the dynamic environments were conducted by Mori and Kita [4], Blackwell et al [3], Branke [39], Cruz et al [40], Nguyen et al [41], and Mavrovouniotis et al [30].…”

“…Several strategies have been adopted to maintain diversity in dynamic optimization problems such as hypermutation in GAs [11], the random immigrant scheme [11], prediction scheme and the memory-based methods, which are considered a special case of it [24], self-adaptive strategy [24], and multipopulation strategy [11,24]. The hypermutation strategy maintains the diversity by increasing the mutation rate for some generations after the dynamic environmental change [11]. The random immigrant strategy maintains the diversity by replacing a part of the population with randomly generated solutions in each generation [11].…”

“…The hypermutation strategy maintains the diversity by increasing the mutation rate for some generations after the dynamic environmental change [11]. The random immigrant strategy maintains the diversity by replacing a part of the population with randomly generated solutions in each generation [11]. The hypermutation and random immigrant have been used for GA and evolutionary algorithms.…”