A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.

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► A new dynamic hybrid PSO-based algorithm (DHPSO) with variable population size is developed.
► Inspired from the microbial life, new particles are born especially in high potential regions and old ones die.
► Using the quadratic interpolation and three neighboring particles, infants are reproduced.
► The population is divided into two subpopulations to perform exploitation and exploration.
► Numerical analyses show DHPSO is efficient for various types of dynamic optimization problems.