Cluster-based differential evolution with Crowding Archive for niching in dynamic environments
Real world optimization problems may very often be dynamic in nature, i.e. the position or height of the optima may change over time instead of being fixed as for static optimization problems. Dynamic Optimization Problems (DOPs) can pose serious challenges to the evolutionary computing community, especially when the search space is multimodal with multiple, time-varying optima. Some recent experimental studies have indicated that the process of evolutionary optimization can benefit from locating and tracking of several local and global optima instead of the single global optimum. This necessitates the integration of specially tailored niching techniques with an Evolutionary Algorithm (EA) for grouping of similar individuals in optimal basins of the landscape against drift and other disruptive forces as well as for making such individuals track the basins whenever dynamic changes appear. Motivated by such requirements, we present a multipopulation search technique involving a clustering strategy coupled with the memory-based Crowding Archive for dynamic niching in non-stationary environments. The algorithm uses Differential Evolution (DE) as its basic optimizer and is referred here as the Cluster-based DE with Crowding Archive (CbDE-wCA). It is equipped with a few robust strategies like favorable solution retention and generation, clearing strategy to eliminate redundant solutions, and crowding to restrict individuals to local search. The performance of the proposed algorithm has been tested on two different instances of the Moving Peaks Benchmark (MPB) problems. Experimental results indicate that CbDE-wCA can outperform other state-of-art dynamic multimodal optimizers in a statistically significant way, thereby proving its worth as an attractive alternative for niching in dynamic environments.
clustering crowding archive niching dynamic optimization problem multimodal optimization