Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problems

Abdel‐Basset, Mohamed; Mohamed, Reda; Zidan, Mahinda; Jameel, Mohammed; Abouhawwash, Mohamed

doi:10.1016/j.cma.2023.116200

Cited by 42 publications

(17 citation statements)

References 114 publications

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“…PSO models the group movement of birds or fish searching for food, ACO is inspired by ants finding the shortest communication path, ABC mimics honey bees’ activities in locating food, and the FA replicates fireflies’ optical communication. Noteworthy wildlife activities, such as foraging, hunting, chasing, migration, and digging, serve as the foundation for swarm-based metaheuristic algorithms like the Pufferfish Optimization Algorithm (POA) [ 15 ], Golden Jackal Optimization (GJO) [ 16 ], Tunicate Swarm Algorithm (TSA) [ 17 ], Coati Optimization Algorithm (COA) [ 18 ], Chameleon Swarm Algorithm (CSA) [ 19 ], Wild Geese Algorithm (WGA) [ 20 ], White Shark Optimizer (WSO) [ 21 ], Grey Wolf Optimizer (GWO) [ 22 ], African Vultures Optimization Algorithm (AVOA) [ 23 ], Mantis Search Algorithm (MSA) [ 24 ], Marine Predator Algorithm (MPA) [ 25 ], Whale Optimization Algorithm (WOA) [ 26 ], Orca Predation Algorithm (OPA) [ 27 ], Reptile Search Algorithm (RSA) [ 28 ], Honey Badger Algorithm (HBA) [ 29 ], and Kookaburra Optimization Algorithm (KOA) [ 30 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Hubálovská,

Hubálovský,

Trojovský

2024

Biomimetics

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This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA’s ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA’s superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA’s effectiveness in real-world optimization tasks.

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Section: Literature Reviewmentioning

confidence: 99%

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Hubálovská,

Hubálovský,

Trojovský

2024

Biomimetics

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“…ABC is designed based on modeling the hierarchical cooperation and activities of colony bees to obtain food resources. Mantis Search Algorithm (MSA) is developed based on modeling the sexual cannibalism and hunting behavior of praying mantises [29]. Genghis Khan Shark Optimizer (GKSO) is inspired, in its design, by the Genghis Khan shark's hunting and self-defense strategy in the wild [30].…”

Section: Literature Reviewmentioning

confidence: 99%

WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems

Benmamoun,

Khlie,

Dehghani

et al. 2024

Mathematics

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Supply Chain (SC) Optimization is a key activity in today’s industry with the goal of increasing operational efficiency, reducing costs, and improving customer satisfaction. Traditional optimization methods often struggle to effectively use resources while handling complex and dynamic Supply chain networks. This paper introduces a novel biomimetic metaheuristic algorithm called the Wombat Optimization Algorithm (WOA) for supply chain optimization. This algorithm replicates the natural behaviors observed in wombats living in the wild, particularly focusing on their foraging tactics and evasive maneuvers towards predators. The theory of WOA is described and then mathematically modeled in two phases: (i) exploration based on the simulation of wombat movements during foraging and trying to find food and (ii) exploitation based on simulating wombat movements when diving towards nearby tunnels to defend against its predators. The effectiveness of WOA in addressing optimization challenges is assessed by handling the CEC 2017 test suite across various problem dimensions, including 10, 30, 50, and 100. The findings of the optimization indicate that WOA demonstrates a strong ability to effectively manage exploration and exploitation, and maintains a balance between them throughout the search phase to deliver optimal solutions for optimization problems. A total of twelve well-known metaheuristic algorithms are called upon to test their performance against WOA in the optimization process. The outcomes of the simulations reveal that WOA outperforms the other algorithms, achieving superior results across most benchmark functions and securing the top ranking as the most efficient optimizer. Using a Wilcoxon rank sum test statistical analysis, it has been proven that WOA outperforms other algorithms significantly. WOA is put to the test with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems to showcase its ability to solve real-world optimization problems. The results of the simulations demonstrate that WOA excels in real-world applications by delivering superior solutions and outperforming its competitors.

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“…This paper utilizes the Mantis Search Algorithm (MSA) to address the CHPED problem and compares it with four other optimizers. The MSA is inspired by the unique behaviors of praying mantises, such as sexual cannibalism and foraging strategies, and it incorporates three optimization stages: hunting, attacking, and cannibalism [38], [39]. Each mantis represents a potential solution which is associated with a position in the search space, which corresponds to a candidate solution.…”

Section: Introductionmentioning

confidence: 99%

“…During the search stage, mantises explore the search space by adjusting their positions using an integration of global and local search strategies. This allows them to efficiently cover the solution space and locate promising regions [38]. The capture stage involves identifying the most favorable solutions, where mantises converge towards the best positions found so far.…”

Section: Introductionmentioning

confidence: 99%

A Novel Mantis Search Algorithm for Economic Dispatch in Combined Heat and Power Systems

Moustafa,

Alnami,

Hakmi

et al. 2024

IEEE Access

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Economic dispatch in combined heat and power (CHP) schemes takes into account the trade-off between generating electricity and producing heat, ensuring that the system operates at the highest level of efficiency and cost-effectiveness. A Mantis Search Algorithm (MSA), in this paper, is developed to address the CHP economic dispatch taking into consideration valve points effect, feasible region constraints for CHP units, and power losses are considered all at once. The MSA draws its inspiration from the unique sexual cannibalism and foraging strategies of praying mantises including three stages of optimization which are hunting, attacking, and cannibalism. Moreover, various recent meta-heuristic optimization algorithms, such as the Aquila Optimizer (AO), Gradient-based Optimizer (GBO), Coot Optimizer (CO), and African Vultures Algorithm (AVO), are utilized for comparative analysis across the two test systems. The developed MSA is demonstrated to be superior across three power and heat loading levels for both a 7-unit system and a larger 84-unit system. In the case of the large 84-unit system, MSA exhibits a significant cost reduction, surpassing AVO, GBO, CO, and AO by 2.017%, 2.511%, 0.817%, and 0.126%, respectively. Simulation results confirm the remarkable efficiency of MSA as a promising solution for economic dispatch in combined heat and power schemes. Testing outcomes indicate that MSA outperforms several previously published methods in the literature, as well as the four adopted optimizers.INDEX TERMS Mantis search optimization algorithm, economic dispatch, combined heat and power units, valve point loading effect.

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Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problems

Cited by 42 publications

References 114 publications

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems

A Novel Mantis Search Algorithm for Economic Dispatch in Combined Heat and Power Systems

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