Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima

Despite the fact that evolutionary algorithms often solve static problems successfully, dynamic optimization problems tend to pose a challenge to evolutionary algorithms [21]. Differential evolution (DE) is one of the evolutionary algorithms that does not scale well to dynamic environments due to lack of diversity [35]. A significant body of work exists on algorithms for optimizing dynamic problems (see Section 1.3). Recently, several algorithms based on DE have been proposed [19][26][28][27][10]. Benchmarks used to evaluate algorithms aimed at dynamic optimization (like the moving peaks benchmark [5] and the generalized benchmark generator [17] [16]), typically focus on problems where a constant number of optima moves around a multi-dimensional search space. While some of these optima may be obscured by others, these benchmarks do not simulate problems where new optima are introduced, or current optima are removed from the search space. Dynamic Population DE (DynPopDE) [27] is a DE-based algorithm aimed at dynamic optimization problems where the number of optima fluctuates over time. This chapter describes the subcomponents of DynPopDE and then investigates the effect of hybridizing DynPopDE with the self-adaptive component of jDE [10] to form a new algorithm, Self-Adaptive DynPopDE (SADynPopDE). The following sections describe dynamic environments and the benchmark function used in this study. Related work by other researchers is presented in Section 1.3. Differential evolution is described in Section 1.4. The components of DynPopDE, the base algorithm used in this study, are described and motivated in Section 1.5. The incorporation of self-adaptive control parameters into DynPopDE to form SA- DynPopDE and the experimental comparison of these algorithms are described in Section 1.6. The main conclusions of this study are summarized in Section 1.7.http://www.springer.com/series/7092hb201

An Heterogeneous Particle Swarm Optimizer With Predator and Scout Particles

Wepresentanewheterogeneousparticleswarmoptimization algorithm, called scouting predator-prey optimizer. The algorithm uses the interactions of the swarm with a predator particle to control the bal- ance between exploration and exploitation. Scout particles are proposed as a straightforward way of introducing new exploratory behaviors into the swarm. These can range from new heuristics that globally improve the algorithm to modifications based on problem specific knowledge. The scouting predator-prey optimizer is compared with several variations of both particle swarm and differential evolution algorithms, using a a set of benchmark functions selected to present the algorithms with different obstacles and difficulties. The experimental results suggest the new opti- mizer can outperform the other approaches over most of the benchmark problems