EOSMA: An Equilibrium Optimizer Slime Mould Algorithm for Engineering Design Problems

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

“…As a result, the overall complexity of MSMA is represented by the notation . The following is a list of the essential components of EOSMA [ 29 ]: initialization, fitness assessment, greedy selection, fitness sorting, fitness weight update, pool update, equilibrium position update, and mutation operator. Initialization has a computational complexity of , greedy selection and equilibrium pool update take time complexity of O(N), updating fitness weight, updating location, and performing a mutation all have a computational complexity of , and sorting fitness has a computational complexity of .…”

“…EOSMA [ 29 ] is an EO-guided SMA that seeks to maximize efficiency by enhancing the search for SMAs. SMA has improved its exploitation and exploration skills to enable better exploitation during the maturation phase and better exploration during the commencement phase.…”

An Improved Solution to Generation Scheduling Problem Using Slime Mold Algorithm

“…This includes techniques such as synergic predator-prey optimization (SPPO) (Singh et al, 2016), seeker optimization algorithm (SOA) (Shaw et al, 2012), genetic algorithm (GA) (Amjady and Nasiri-Rad, 2010), (Elsayed et al, 2014), evolutionary programming (EP) (Sinha et al, 2003), firefly algorithm (FA) (Yang et al, 2012), particle swarm optimization (PSO) (Neyestani et al, 2010), (Safari and Shayeghi, 2011), (Wang and Singh, 2009), artificial bee colony (ABC) (Aydın and Özyön, 2013), colonial competitive differential algorithm (CCDE) (Ghasemi et al, 2016), bacterial foraging algorithm (BFA) (Farhat and El-Hawary, 2010), improved Tabu search algorithm (ITS) (Whei-Min Lin et al, 2002), ant colony optimization (ACO) (Pothiya et al, 2010), group search optimizer (GSO) (Zare et al, 2012), harmony search algorithm (HAS) (Jeddi and Vahidinasab, 2014), biogeographybased optimization (BBO) (Bhattacharya and Chattopadhyay, 2010), and differential evolution (DE) (Jiang et al, 2013). Many researchers used slime mould algorithm to bring better results and few such algorithms are Dispersed Foraging Slime Mould Algorithm (DFSMA) (Hu et al, 2022), Chaos-oppositionenhanced slime mould algorithm (CO-SMA) (Rizk, 2022), Opposition based learning slime mould algorithm (OBLSMA) (Houssein et al, 2022), Multi-objective slime mould algorithm (MOSMA) (Houssein et al, 115870), Equilibrium optimizer slime mould algorithm (EOSMA) (Yin et al, 2022). In this work, SMA is used to identify solutions to economic load dispatch problems on a variety of test systems.…”

Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions

“…Optimization has received more attention in recent years, and various new optimization methods have been developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].These newly discovered techniques are applied to real-world challenges. An optimization problem is about finding the optimal answer from a collection of possible solutions.…”

“…By evaluating the success of newly developed meta-heuristic optimization algorithms and comparing them with previously published algorithms, new studies on improving existing optimization algorithms or developing new optimization algorithms based on successful algorithms are added to the literature on a daily basis. In this context, Artificial Water Drop Algorithm [28], Mexican Axolotl Optimization: a novel bio-inspired heuristic [3], Tunicate Swarm Algorithm [2], Tuna Swarm Optimization [1,8], Equilibrium Slime Mould Algorithm [8,26], Dingo Optimization Algorithm [29], Leader Harris hawks optimization [5], Differential Squirrel Search Algorithm [9,10], Leader Slime Mould Algorithm [31], Adaptive Opposition Slime Mould Algorithm [32], CLA-New Meta-Heuristic Algorithm [38], Hybrid Augmented Grey Wolf Optimizer and Cuckoo Search [33,34], Child Drawing Development Optimization Algorithm [34], Golden Eagle Optimizer [35], Bald eagle search Optimization algorithm [14], Chimp Optimization Algorithm [39], Lévy Flight Distribution [6] and Harris hawks optimization [36] are some of them. This paper compares the performances of sixteen meta-heuristic optimization algorithms presented in the literature between 2021 and 2022.…”