A new optimization algorithm inspired by the quest for the evolution of human society: Human felicity algorithm

Kazemi, Mohammad; Veysari, Elham Fazeli

doi:10.1016/j.eswa.2021.116468

Cited by 22 publications

(3 citation statements)

References 59 publications

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“…Tabu Search [67] TS Glover F Harmony Search [68] HS Geem ZW, Kim JH, Loganathan G Seeker optimization algorithm [69] SOA Dai C, Zhu Y, Chen W Imperialistic Competitive Algorithm [70] ICA Atashpaz-Gargari E, Lucas C Biogeography-Based Optimization [71] BBO Simon D League Championship Algorithm [72] LCA Husseinzadeh A Fireworks algorithm [73] FA Tan Y, Zhu Y Group Counseling Optimization [74] GCO Eita MA, Fahmy MM Teaching Learning Based Optimization [75] TLBO Rao RV, Savsani VJ, Vakharia DP Social-Based Algorithm [76] SBA Ramezani F, Lotfi S Mine Blast Algorithm [77] MBA Sadollah A, Bahreininejad A, Eskandar H, Hamdi M Exchange Market Algorithm [78] EMA Ghorbani N, Babaei E Interior Search Algorithm [79] ISA Gandomi AH Soccer League Competition Algorithm [80] SLCA Moosavian N, Roodsari BK Human Behavior-Based Optimization [81] HBBO Ahmadi SA Search And Rescue [82] SAR Shabani A, Asgarian B, Gharebaghi SA, Salido MA, Giret A Forensic-Based Investigation [83] FBI Chou JS, Nguyen NM Puzzle Optimization Algorithm [84] POA Ahmadi Zeidabadi F, Dehghani M Human felicity algorithm [85] HFA VerijKazemi M, FazeliVeysari E…”

Section: Human-based Algorithms (Hba)mentioning

confidence: 99%

PMW: The Portuguese Man o' War optimization algorithm

MiarNaeimi,

salehnia,

Hamzehkolaei

et al. 2024

Preprint

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In the present study, the Portuguese Man o' War (PMW) movement on the fishes hunting route was used as a source of inspiration for the development of the PMW optimization algorithm. The Portuguese Man o' War is a marine creature that moves on the surface of the ocean under the wind force, wave surface currents, wave tension and diffusion current. The PMW feeds of hunting the fishes by poisonous strings. Following the movement of this sea creature, it was realized that there was an "irregular discipline" in its life cycle. In this way, in this paper, by simulating the movement of PMWs along with the use of several innovative solutions, a robust and powerful algorithm was created in two phases of exploration and exploitation. The performance of PMW was discussed by unbound test functions as well as bound engineering problems and compared with the results of other new algorithms. The results show that the proposed algorithm performs better or equal to the existing algorithms, both in terms of obtaining the final response and in reducing the number of calls to the objective function.

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Section: Human-based Algorithms (Hba)mentioning

confidence: 99%

PMW: The Portuguese Man o' War optimization algorithm

MiarNaeimi,

salehnia,

Hamzehkolaei

et al. 2024

Preprint

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“…Human-based MAs imitate some human activities and behaviors. Socio Evolution and Learning Optimization (SELO) 33 , Social Network Search (SNS) 34 , and Human Felicity Algorithm (HFA) 35 are some examples of this category. Swarm-based MAs mimic the social behaviors of animals or organisms living in swarms, communities, or packs 36 .…”

Section: Related Workmentioning

confidence: 99%

Red-tailed hawk algorithm for numerical optimization and real-world problems

Ferahtia,

Houari,

Rezk

et al. 2023

Sci Rep

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This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.

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“…( 2007 ) 267 Human Felicity Algorithm (HFA) Veysari et al. ( 2022 ) 268 Human Group Formation (HGF) Thammano and Moolwong ( 2010 ) 269 Human Mental Search (HMS) Mousavirad and Ebrahimpour ( 2017 ) 270 Human-Inspired algorithm (HIA) Zhang et al. ( 2009 ) 271 Hunting Search (HuS) Oftadeh et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

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

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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A new optimization algorithm inspired by the quest for the evolution of human society: Human felicity algorithm

Cited by 22 publications

References 59 publications

PMW: The Portuguese Man o' War optimization algorithm

PMW: The Portuguese Man o' War optimization algorithm

Red-tailed hawk algorithm for numerical optimization and real-world problems

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

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