Soft Island Model for Population-Based Optimization Algorithms

Population-based optimization algorithms adopt a regular network as topologies with one set of potential solutions, which may encounter the problem of premature convergence. In order to improve the performance of optimization techniques, this paper proposes a soft island model topology. The initial population is virtually separated into several subpopulations, and the connection between individuals from subpopulations is probabilistic. The workability of the proposed model was demonstrated through its implementation to the Particle Swarm Optimization and Differential Evolution algorithms and their modifications. Experiments were conducted on benchmark functions taken from the CEC’2017 competition. The best parameters for the new topology adaptation mechanism were found. Results verify the effectiveness of the population-based algorithms with the proposed model when compared with the same algorithms without the model. It was established that by applying this topology adaptation mechanism, the population-based algorithms are able to balance their exploitation and exploration abilities during the search process.