A New Metaheuristic Bat-Inspired Algorithm

By Xin-She Yang. Nature Inspired Cooperative Strategies for Optimization, 2010

4,996 citations recorded by the source corpus.

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/860227a393a1476c73b1a544d215ae8a20dc1bb9

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Citations in the corpus

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 Table 4 Properties of benchmark functions, lb denotes lower bound, ub denotes upper bound, opt denotes optimum point.”
Black hole: A new heuristic optimization approach for data clustering (2013, background): “The capability of the echolocation of bats is fascinating as they can find their prey and recognize different types of insects even in complete darkness [31].”
SCA: A Sine Cosine Algorithm for solving optimization problems (2016, methodology): “The SCA algorithm is compared Firefly Algorithm (FA) [67], Bat Algorithm (BA) [68], Flower Pollination Algorithm (FPA) [69], Gravitational Search Algorithm (GSA) [54], PSO and GA for verification of the results.”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “Yang (2010) 35.”
Firefly Algorithm: Recent Advances and Applications (2013, methodology): “Among these new algorithms, it has been shown that ﬁreﬂy algorithm is very eﬃcient in dealing with multimodal, global optimisation problems.”
Review of Swarm Intelligence-based Feature Selection Methods (2020, methodology): “The Firefly Algorithm (FA) was introduced by Xin-She Yang, in 2010 (Yang 2010), that, the main idea was inspired by the optical connection between fireflies.”
Flower Pollination Algorithm for Global Optimization (2012, methodology): “For example, genetic algorithms were based on the Darwinian evolution of biological systems [9] and particle swarm optimization was based on the swarm behaviour of birds and fish [11, 12], which bat algorithm was based on the echolocation behaviour of microbats [21] and firefly algorithm was based o”
Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems (2017, methodology): “Other swarm intelligence techniques in the literature are: Artificial Bee Colony (ABC) algorithm [11] , Cuckoo Search (CS) algorithm [12] , Firefly Algorithm (FA) [13] , Bat Algorithm (BA) [14] , Grey Wolf Optimizer (GWO) [15–17] , Dolphin Echolocation (DE) [18] , Whale Optimization Algorithm (WOA) ”
On the exploration and exploitation in popular swarm-based metaheuristic algorithms (2018, background): “The rest of the algorithms are artificial bee colony (ABC) [9], ant colony optimization (ACO) [8], cuckoo search (CS) [10], firefly algorithm (FA) [11], fireworks algorithm (FWA) [12], bat algorithm (BA) [13], teaching-learning & Mohd Najib Mohd Salleh najib@uthm.”
Emperor penguin optimizer: A bio-inspired algorithm for engineering problems (2018, background): “Bat-inspired Algorithm (BA) [27] is inspired by the echolocation behavior of bats.”