The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

Abstract

Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co.

Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations

“…As a result of these impressive results on complex multi-dimensional engineering problems, this study's hypothesis regarding the WCA [62] Water Cycle Algorithm 50 N sr = 4, D max = 10 −5 10 GTO [63] Artificial Gorilla Troops Optimiser 50 p = 0.03, β = 3, ω = 0.8 11 GWO [6] Gray Wolf Optimiser 50 A = 2 × α × rand() − α, C = 2 × rand(), α linearly decreases from 2 to 0 12 MFO [49] Moth Flame Optimiser 50 Flame no = N − iter × ((N − 1)/M ax iter )); ,α linearly decreases from -1 to -2 13 MVO [51] Multi Verse Optimiser 50 minimum and maximum of Wormhole existence probability: WEP M ax = 1, WEP M in = 0.2, ρ = 6. 14 EO [33] Equilibrium Optimiser 50 ω 1 = 1, ω 2 = 2, GP = 0.5(=generation probability), V = 1 15 CO [64] Cheetah Optimiser 50 predefined settings selection and enhancement of MPA's performance has been confirmed. The convergence rate of optimisation algorithms implemented for the 260-bar truss problem can be shown in Figure 14.…”

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

“…( 2018 ) 113 Cheetah Chase Algorithm (CCA) Goudhaman ( 2018 ) 114 Cheetah Optimizer (CO) Akbari et al. ( 2022 ) 115 Chef-Based Optimization Algorithm (CBOA) Trojovská and Dehghani ( 2022 ) 116 Chemical Reaction Optimization (CRO) Alatas ( 2011 ) 117 Chicken Swarm Optimization (CSO) Meng et al. ( 2014 ) 118 Child Drawing Development Optimization (CDDO) Abdulhameed and Rashid ( 2022 ) 119 Chimp Optimization Algorithm (ChOA) Khishe and Mosavi ( 2020 ) 120 Circle Search Algorithm (CSA) Qais et al.…”

Game Theory-Based Bidding Strategy in the Three-Level Optimal Operation of an Aggregated Microgrid in an Oligopoly Market

“…10. A comparison study is performed by deploying the proposed particle swarm optimization (PSO) and cheetah optimizer (CO) method [45], i.e., powerful evolutionary methods, on (21) as the objective function of the first-level optimization problem considering ( 22)-( 24) as the problem constraints and ( 25), ( 26) and ( 27) as the objective function and constraints of the second-level optimization problem. The achieved results of the proposed bidding strategy and PSO and CO algorithms are shown in Tables VII and VIII, respectively.…”