Continuous Population-Based Incremental Learning with Mixture Probability Modeling for Dynamic Optimization Problems

This paper proposes a multimodal extension of PBILC based on Gaussian mixture models for solving dynamic optimization problems. By tracking multiple optima, the algorithm is able to follow the changes in objective functions more efficiently than in the unimodal case. The approach was validated on a set of synthetic benchmarks including Moving Peaks, dynamization of the Rosenbrock function and compositions of functions from the IEEE CEC’2009 competition. The result obtained in the experiments proved the efficiency of the approach in solving dynamic problems with a number of competing peaks.