Inducing Niching Behavior in Differential Evolution Through Local Information Sharing

Abstract

In practical situations it is very often desirable to detect multiple optimally sustainable solutions of an optimization problem. The population-based evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations to aid the parallel localized convergence of population members around different basins of attraction. This paper presents an improved information sharing mechanism among the individuals of an evolutionary algorithm for inducing efficient niching behavior. The mechanism can be integrated with stochastic real-parameter optimizers relying on differential perturbation of the individuals (candidate solutions) based on the population distribution. Various real-coded Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) fit the example of such algorithms. The main problem arising from differential perturbation is the unequal attraction towards the different basins of attraction which is detrimental to the objective of parallel convergence to multiple basins of attraction. We present our study through DE algorithm owing to its highly random nature of mutation, and show how population diversity is preserved by modifying the basic perturbation (mutation) scheme through the use of random individuals selected probabilistically. By integrating the proposed technique with DE framework, we present three improved versions of well-known DE-based niching methods. Through an extensive experimental analysis, a statistically significant improvement in the overall performance has been observed upon integrating of our technique with the DE-based niching methods.

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

“…We can see a common feature among most of these PSO, DE, and ES niching algorithms, that is, the need to identify nearest neighbourhood best points with respect to a current point in the population, e.g., FER-PSO [33], LIPS [34], DE/nrand/1 [52], dADE/nrand/1 [53], LoINDE [56], a DE using a proximity mutation operator [50], a Local selectionbased DE [49], and NEA2 (Niching with CMA-ES via NBC) [95]. These neighbourhood best points can be subsequently used to attract individuals in their respective neighbourhoods, in order to achieve the niching effect.…”

“…Similarly, Biswas et al [56] recently proposed three niching DE variants by incorporating a probabilistic parent selection scheme based on fitness and proximity information, known as localized shared information. More specifically, parent selection in the mutation stage is replaced by a probabilistic scheme to increase the probability of selecting fitter individuals that are closer to the target vector.…”

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

“…In order to avoid the time complexity caused by pairwise distance calculations, the work [27] proposes a fast niching technique by introducing the locality sensitive hashing, which is an efficient algorithm for approximately retrieving nearest neighbors. An improved information-sharing mechanism among individuals is introduced in [28] to induce more stable and efficient niching behavior, and a newly proposed parent-centric mutation operator is combined with a synchronous crowding replacement rule in [29]. Besides niching methods, new paths to gain the multimodal optimization ability are established.…”

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

“…The DE/rand/1 operator is one of the most commonly used DE variants [62], and all different solutions are randomly chosen from the population. So, this strategy does not generate biased or special search directions, and then a new direction is selected at random each time.…”