Double-Particle Swarm Optimization with Induction-Enhanced Evolutionary Strategy to Solve Constrained Optimization Problems

This paper presents double-particle swarm optimization (PSO) with induction-enhanced evolutionary strategy (DIEPSO) to solve global nonlinear optimization problems. A new PSO algorithm with induction-enhanced evolutionary strategy (IEPSO) is constructed. In the algorithm, a deterministic selection strategy is proposed to ensure the diversity of population. Meanwhile, the induction of evolving direction is enhanced by adding gene-adjusting and adaptive focus-varied tuning operator. The constraint handling approach uses double-particle swarm searching mechanism. It guides the searching process towards the feasible region. Also, a simple diversity mechanism is added, which properly allows some particles of infeasible region to be preserved in the feasible region. It makes the particles close to the boundary of the feasible region. The approach has been tested on four problems commonly used in the literature, and compared with other approaches. Results indicate that the approach is competitive and easy to be implemented.