Biogeography-based optimisation with chaos

By Shahrzad Saremi, Seyedali Mirjalili, Andrew Lewis. Neural computing & applications (Print), 2014

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Chaotic Local Search-Based Differential Evolution Algorithms for Optimization (2021, methodology): “Chaotic properties have been used in other newly proposed swarm intelligence optimization algorithms, such as chaotic BBO [40], chaotic bat algorithm [29], [41], chaotic cuckoo search [42], chaotic fruit ﬂy optimization algorithm [43], chaotic krill herd algorithm [44], ﬁreﬂy algorithm with chaos…”
Elephant Herding Optimization (2015, methodology): “By comparing with BBO [53-58], DE [59-64] and GA [65], the performance of EHO is investigated by several experiments implemented on fifteen test cases.”
Evolutionary population dynamics and grey wolf optimizer (2014, background): “two tables, the test functions are divided to two groups: unimodal and multi-modal [56–58].”
Chaotic grasshopper optimization algorithm for global optimization (2019, methodology): “Some major efforts in this area include PSO [27], FA [26], BOA [28], GWO [29], GAs [30], big bang–big crunch algorithm [31], hybridizing chaotic sequences with memetic differential evolution algorithm [32], water cycle algorithm [33], imperialist competitive algorithm [34], artificial immune system ”
SCA: A Sine Cosine Algorithm for solving optimization problems (2016, methodology): “Popular methods in this class are: chaotic maps [13-17], evolutionary operators [18-23], and local searches [24-27].”