Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: June 15, 2026
Pages: 51–56
The Bat Algorithm (BA), introduced as a nature-inspired metaheuristic based on idealized echolocation, has developed into a family of continuous, binary, discrete, multi-objective, and hybrid optimization methods. This survey reviews the decade from 2015 to 2025, emphasizing how researchers have addressed exploration–exploitation balance, premature convergence, parameter sensitivity, scalability, and problem-specific representations. The review synthesizes primary literature and applications across engineering design, power and energy systems, image processing, medical analytics, feature selection, classification, clustering, and combinatorial scheduling. The study identifies key challenges in reproducibility, benchmarking standards, and algorithmic hybridization.
Bat Algorithm, Bat-Inspired Optimization, Metaheuristics, Binary Optimization, Hybrid Algorithms, Feature Selection, Swarm Intelligence
Nature-inspired metaheuristics are widely used for complex optimization problems where exact analytical methods are computationally prohibitive or where the search landscape is nonlinear, discontinuous, multimodal, or constrained. Yang's original Bat Algorithm models echolocation behaviour through frequency, velocity, position, loudness, and pulse-emission rate [1].
Over the decade from 2015 to 2025, the Bat Algorithm has evolved from initial canonical formulations into sophisticated modified, binary, discrete, and hybrid architectures [2,3]. This survey organizes the primary developmental directions across this decade, examining how hybridization with genetic algorithms, differential evolution, and local search heuristics has tackled premature convergence and high-dimensional parameter search spaces.