Literature DB >> 34283111

Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization.

Mohammad Dehghani1, Pavel Trojovský2.   

Abstract

Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimization Algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.

Entities:  

Keywords:  optimization; optimization algorithm; optimization problem; population-based; teamwork

Year:  2021        PMID: 34283111     DOI: 10.3390/s21134567

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  5 in total

1.  A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems.

Authors:  Mohammad Dehghani; Štěpán Hubálovský; Pavel Trojovský
Journal:  PeerJ Comput Sci       Date:  2022-03-07

2.  A new optimization algorithm based on mimicking the voting process for leader selection.

Authors:  Pavel Trojovský; Mohammad Dehghani
Journal:  PeerJ Comput Sci       Date:  2022-05-13

3.  A new human-based metahurestic optimization method based on mimicking cooking training.

Authors:  Eva Trojovská; Mohammad Dehghani
Journal:  Sci Rep       Date:  2022-09-01       Impact factor: 4.996

4.  A new human-inspired metaheuristic algorithm for solving optimization problems based on mimicking sewing training.

Authors:  Mohammad Dehghani; Eva Trojovská; Tomáš Zuščák
Journal:  Sci Rep       Date:  2022-10-17       Impact factor: 4.996

5.  Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems.

Authors:  Mohammad Dehghani; Pavel Trojovský
Journal:  Sensors (Basel)       Date:  2022-02-24       Impact factor: 3.576

  5 in total

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