Bat algorithm: a novel approach for global engineering optimization

By Xin-She Yang, Amir H. Gandomi. 2012

1,848 citations recorded by the source corpus.

– Nature‐inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks., – The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature., – BA has been carefully implemented and carried out optimization for eight well‐known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms., – The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/57d84022cac794eeb753f7812df416343e829848

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Civil and Structural Engineering

In Computational Theory and Mathematics

Citations in the corpus

Spider wasp optimizer: a novel meta-heuristic optimization algorithm (2023, methodology): “2014), Bat algorithm (Yang and Gandomi 2012), cuckoo search algorithm (Gandomi et al.”
Discrete cuckoo search algorithm for the travelling salesman problem (2014, background): “Metaheuristics are usually simple to implement, but they often can solve complex problems and can thus be adapted to solve many real-world optimization problems, from the fields of operations research, engineering to artificial intelligence [10, 11, 12, 36].”
Water wave optimization: A new nature-inspired metaheuristic (2015, methodology): “It should be noted that most comparative algorithms such as BA perform well on basic unimodal functions [14], but lose their performance on these three rotated unimodal functions.”
Krill herd: A new bio-inspired optimization algorithm (2012, background): “Several extensions to the major categories of the swarm algorithms have been presented in the literature [18].”
Chaos-enhanced accelerated particle swarm optimization (2013, background): “Intensification and diversification are two important characteristics of metaheuristic methods [5].”
The Arithmetic Optimization Algorithm (2021, background): “…[33] • Biogeography-based Optimization (BBO) [34] • Flower Pollination Algorithm (FPA) [35] • Grey Wolf Optimizer (GWO) [36] • Bat Algorithm (BAT) [37] • Firefly Algorithm (FA) [38] • Cuckoo Search Algorithm (CS) [39] • Moth-Flame Optimization (MFO) [40] • Gravitational Search Algorithm (GSA)…”
Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, methodology): “Random permutations are used in many algorithms, such as DE and FPA.”
Multi-Objective Flower Algorithm for Optimization (2014, positive): “Nature-inspired algorithms have shown their promising performance and have thus become popular and widely used, and these algorithms are mostly swarm intelligence based [4, 8, 22, 29, 30, 31, 32, 12, 13].”
An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems (2016, result): “Some of these fields are the continuous optimization, in which some additional works have been published apart from to the original one, ([Bora et al.(2012)Bora, Coelho & Lebensztajn, Yang & Hossein Gandomi(2012)]), combinatorial optimization ([Marichelvam et al.(2013)Marichelvam, Prabaharan, Yang &”
Chaotic Krill Herd algorithm (2014, methodology): “The methods included in the comparative study are ACO [4], BA [51,52], CS [49], DE [30,38], ES [1,2], GA [18], KH [8], PBIL [36], and PSO”