Mountaineering Team-Based Optimization

Version 1.0.1 (6.25 KB) by iraj faraji
A Novel Human-based Metaheuristic Algorithm
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Updated 8 Mar 2023

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a new optimization algorithm is introduced in the field of evolutionary computations, which is based on intellectual and environmental evolution with coordinated human behavior. A mountaineering team consists of a number of mountaineers with an experienced and professional leader whose goal is to conquer the mountaintop in the region, where the mountaintop is considered the final global solution to the optimization problem . Like other evolutionary optimization methods, the developed algorithm starts with some initial population. In this algorithm, each population member is called a mountaineering team member or mountaineer. The core of this algorithm is the regular and coordinated movement of mountaineers and the consideration of natural phenomena. According to the regular and coordinated movement phase, the mountaineers are coordinated by their teammates and also the group leader, which in optimization science is equivalent to the best solution in the current iteration of the algorithm to reach their goal, which is to conquer the mountaintop, or in optimization science to reach the global optimum or the best solution. In presenting this algorithm, natural disasters such as avalanches are also considered, which can hinder the progress of mountaineers and even endanger their lives. The main inspiration of the MTBO algorithm is the team's orderly and coordinated movement to conquer the mountaintop, taking into account natural disasters.
Faridmehr, Iman, Moncef L. Nehdi, Iraj Faraji Davoudkhani, and Alireza Poolad. 2023. "Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm" Mathematics 11, no. 5: 1273.

Cite As

Faridmehr, Iman, et al. “Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm.” Mathematics, vol. 11, no. 5, MDPI AG, Mar. 2023, p. 1273, doi:10.3390/math11051273.

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Version Published Release Notes
1.0.1

Paper link update

1.0.0