A review of applications of animal-inspired evolutionary algorithms in reservoir operation modelling

Abstract

Successful operation of reservoir systems to guarantee the optimal use of available water resources has been the subject of many studies. The advent and applications of evolutionary algorithms (EAs) in the field of reservoir operation have led to significant advances in our capacity to improve the planning and management of complex reservoir systems. This study reports a review of the applications of animal-inspired EAs to reservoir operation optimization selected among a large number of available papers in this area of research. The animal-inspired EAs herein identified concern algorithms that mimic biologic traits of animal (wild) species. Among the animal-inspired EAs ant colony optimization (ACO), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), honey bee mating optimization (HBMO), firefly algorithm (FA), cuckoo search (CS) and the bat algorithm (BA) are the best-known ones selected for this review. This paper presents a brief description of the algorithmic characteristics and various employed improved versions or varieties thereof of each of the stated EAs. Furthermore, the differences between the proposed animal-inspired EAs and their improved versions are identified by comparing the performance of the implemented animal-inspired EAs in the reviewed literature. PSO and its varieties have the largest number of reported applications. Our comparison results revealed that constrained, discrete and randomized varieties of the animal-inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time. Moreover, all the animal-inspired EAs outperformed traditional methods of reservoir optimization, such as nonlinear programming (NLP) and dynamic programming (DP).