Emperor penguin optimizer: A bio-inspired algorithm for engineering problems

A New Metaheuristic Inspired by the Vapour-Liquid Equilibrium for Continuous Optimization

“…The (TPD) indicator was employed only for those functions whose optima (Opt) were different than zero. For the remaining functions, as the success indicator, we calculated the (DMO), the difference between the minimum achieved by VLE (Min) and the optimal value published (Opt) (35). Figure 17 shows the convergence graphics obtained by VLE for benchmark functions f 1 , f 2 , f 7 , f 8 , f 10 , and f 12 .…”

“…Algorithm 1 details the search procedure of VLE metaheuristic. initialize lists, neighbourhood, accountants (m = 1, r = 1), totalizer of variables (tvar = 0) and status of variables (svar(d) = 0) 4: generate an initial solution randomly 5: evaluate the fitness of the current solution 6: update the list of accepted and best movements with the current solution 7: update the list of rejected movements with 8: update the best solution with the current solution 9: characterise the chemical components 10: create the wide area neighbourhood of solutions for each decision variable 11: while (1) do 12: if or (m > M, r > R) then 13: break 14: end if 15: if (tvar < D) then 16: search the best change for each decision variable in its corresponding neighbourhood 17: else 18: create a local area neighbourhood for each decision variable with closer neighbours 19: end if 20: evaluate the fitness of the current solution 21: evaluate the fitness change as f cha = f it − b f it 22: if ( f cha < 0) then 23: if ( f cha < δ) then 24: make a downhill movement (a new best solution), and update the records 25: else 26: make an uphill movement 27: end if 28: else 29: make an uphill movement 30: end if 31: display e, m, r, b f it 32: end while 33: save e, x, b f it, convergence graphic 34: end for 35: sort b f it 36: save box plot 37: save statistics Algorithm 2 describes the search procedure of the best change for each decision variable. This is done by sorting the saved values of the objective function when creating or updating the search neighbourhood.…”

“…One of them considers, as a source of inspiration, human reasoning to make decisions when faced with fuzzy data [28]. Some of these techniques are: grey wolf optimizer (GWO) [29] (it imitates the command hierarchy and hunting strategy of grey wolves), the pity beetle algorithm (PBA) [30] (it was inspired by the grouping behaviour of the beetle Pityogenes chalcographus, looking for food and nests), shark smell optimization (SSO) [31] (it simulates the skill of a shark for finding their prey by using its sense of smell and moving toward the source of the odour), symbiotic organisms search (SOS) [32] (mimics the symbiotic interaction strategies followed by organisms to survive and propagate in the ecosystem), dolphin echolocation (DOE) [33] (it considers the echolocation system used by dolphins in searching for food), the whale optimization algorithm (WOA) [34] (it mimics the social behaviour of humpback whales), and the emperor penguin optimizer (EPO) [35] (it simulates the huddling behaviour of emperor penguins (Aptenodytes forsteri)).…”

EPO: An Optimization technique for Urban Traffic Management While Limiting the Pollution using WSN

“…EPO is very recent algorithm especially developed for optimization purpose. Actually, it shows better performance than other existing scheme 21 . Using EPO, the time complexity is reduced at the same time throughput is increased.…”

Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer

“…Simulated Annealing [12] SA Gravitational Search Algorithm [13] GSA Charged System Search [14] CSS Black Hole Algorithm [15] BH Emperor Penguin Optimizer [16] EPO Artificial Chemical Reaction Optimization Algorithm [17] ACROA Ray Optimization Algorithm [18] RO Galaxy-Based Search Algorithm [19] GbSA Ant Colony Optimization [20] ACO Cuckoo Search [21] CS Bat-Inspired Algorithm [22] BA Firefly Algorithm [23] FA Spotted Hyena Optimizer [24] SHO Exchange Market Algorithm [25] EMA Social-Based Algorithm [26] SBA Harmony Search [27] HS Grey Wolf Optimizer [28] GWO Mine Blast Algorithm [29] MBA Every optimization algorithm needs to address the exploration and exploitation of a search space [30] and maintains a good balance between exploration and exploitation. The exploration phase investigates the different promising regions in a search space, whereas exploitation searches the close global optimal solutions around the promising regions [31,32].…”