Circular structures of puffer fish: A new metaheuristic optimization algorithm

Optimal Integration of Photovoltaic Sources in Distribution Networks for Daily Energy Losses Minimization Using the Vortex Search Algorithm

“…where σ 0 is also considered the initial radius r 0 of the outer hyper ellipse, that is, the first solution space [34]. As a first step, by multiplying the initial radius by Equation (12), this solution space is expanded to obtain complete coverage. The set of neighboring solutions obtained and contained in C t i (x), verified within the algorithm and taking into account the lower and upper limits set in µ 0 , are defined by Equation (11).…”

“…Subsequently, in the hyper-ellipsoid, the effective radius is reduced to r t , so it must readjust the new radio to suit the process of searching for neighboring solutions through the inverse incomplete gamma function during each iteration. Equation (12) represents the incomplete gamma function.…”

“…These algorithms use advantages that vary according to the implemented numerical optimization methods, which are adaptable algorithms to any problem. This variety allows finding a space solution that reduces losses [12] and costs of the distributed generation [13], which is because the algorithm provides the information necessary to determine the optimal solution.…”

Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm

“…Various other meta-heuristic algorithms that have been developed are the Slime Mould (SM)-based optimization algorithm, Modified Monarch Butterfly Optimization (MMBO) algorithm, Backtracking Search Algorithm combined with Burger's Chaotic map (BSABCM), Particle Swarm Optimization (PSO) [15], Genetic Algorithms (GA) [16], Chaotic Mayflies Optimization (CMO) algorithm [17], Grey Wolf Optimizer (GWO) [18], Whale Optimization Approach (WOA) [19], Salp Swarm Optimization algorithm (SSOA) [20], Effective Informed Adaptive PSO (EIA-PSO) [21], Cuckoo Search-Ant Colony Optimization (CS-ACO) [22], bi-inspired algorithm [23], Tribe PSO Algorithm (T-PSO) [24], Bi-Subgroup Optimization (BSO) [25], Sine Cosine Algorithm (SCA) [26], and Modified Owl Search Algorithm (DOSA) [27]. Another meta-heuristic algorithm, for example Puffer Fish (PF) [28], based on the behavior of male puffer fish in order to charm the female puffer fish by building special circular structures on the seabed, was developed for finding the optimal solution set to the problems of optimization. Various hybrid algorithms, i.e., Particle Swarm Optimization Grey Wolf Optimizer (PSOGWO) [29] and Grey Wolf Optimizer Cuckoo Search (GWOCS) [30], were also developed for the same reason and performed significantly better when identifying the optimal solution.…”

Νοήμονες Υπολογιστικές Μέθοδοι Εμπνευσμένες Από Τον Φυσικό Κόσμο Για Την Βελτιστοποίηση Συστημάτων

“…Οι Połap and Woz´niak (2017) Serani and Diez (2018). Άλλοι αλγόριθμοι που προτάθηκαν την ίδια χρονιά είναι ο Αλγόριθμος Βελτιστοποίησης Βασισμένος στον Γατόπαρδο (Cheetah Based Optimization Algorithm) (Klein, Mariani and Coelho, 2018), οι Κυκλικές Δομές Τετραοδοντίδων (Circular Structures of Puffer Fish) (M. C. Catalbas and A. Gulten, 2018), ο Αλγόριθμος Ψαριού που αναπαράγεται από το Στόμα (Mouth Brooding Fish algorithm) (Jahani and Chizari, 2018) και ο Αλγόριθμος του Σκαθαριού Pity (Pity Beetle Algorithm) (Kallioras, Lagaros and Avtzis, 2018 Koohi et al, 2019), ο Αλγόριθμος Βελτιστοποίησης Θαλάσσιου Λέοντα (Sea Lion Optimization Algorithm) (Masadeh, 2019) και ο Αλγόριθμος Αναζήτησης Σκίουρου (Squirrel Search Algorithm) (Jain, Singh and Rani, 2019).…”