Dynamic Constrained Optimization with offspring repair based Gravitational Search Algorithm

Dynamic Constrained Optimization Problems (DCOP) are a unique class of optimization problems where the objective function as well as the constraint functions change with respect to time. Conventional DCO algorithms involve Genetic Algorithms (GAs) accompanied by a separate constraint-handling technique e.g., a repair method, or a penalty function. However, ordinary repair methods with elitism significantly decrease the diversity of the population during the exploitation stage and penalty functions cannot properly deal with disconnected feasible regions. In this paper, we propose a new approach based on the Gravitational Search Algorithm as well as a modified version of a repair method that produces improved results. The proposed approach incorporates knowledge-reusing and knowledge-restarting in order to produce a quick recovery and faster convergence.