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

Trojovská

Zuščák

2022

Preprint

Self Cite

This paper introduces a new human-based metaheuristic algorithm called Sewing Training-Based Optimization (STBO). The fundamental inspiration of STBO is the process of teaching sewing to beginner tailors. The process is described in three phases: (i) training, (ii) imitation of the instructor's skills, and (iii) practice, and is then mathematically modeled. STBO performance is evaluated on twenty-three objective functions of the types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The optimization results show that STBO, with its high power of exploration and exploitation, has provided suitable solutions for benchmark functions. Also, to evaluate the quality of STBO, the results are compared with ten well-known metaheuristic algorithms. Furthermore, the simulation results show that STBO has a much more competitive performance than competitor algorithms by providing superior results. Finally, the implementation of STBO in solving four engineering design problems demonstrates the capability of the proposed approach in dealing with real-world applications.

Section: Literature Reviewmentioning

confidence: 99%

A New Human-Inspired Metaheuristic Algorithm for Solving Optimization Problems Based on Mimicking Sewing Training

Trojovská

Zuščák

2022

Preprint

Self Cite

“…The educational environment of the classroom and the exchange of knowledge between the teacher and the students and also among students, have been a good inspiration source for the Teaching-Learning Based Optimization (TLBO) [28]. Collaboration between members of a team and presenting teamwork applied with the aim to achieve the assigned goal set to the team is the main idea of the Teamwork Optimization Algorithm (TOA) [29] and the Election Based Optimization Algorithm (EBOA) [30]. The War Strategy Optimization (WSO) [31] is based on the strategic movement of army troops during the war.…”

Section: Literature Reviewmentioning

confidence: 99%

On a New Human-Based Metaheuristic Optimization Algorithm Based on Imitating Foreign Language Education Process

Trojovský

Trojovská

et al. 2022

Preprint

Self Cite

The paper presents a new human-based metaheuristic algorithm called Language Education Optimization (LEO). LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules. LEO is mathematically modeled in three phases: (i) students selecting their teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search and exploitation in local search. The performance of LEO in optimization tasks has been challenged against twenty-three benchmark functions of a variety of unimodal and multimodal types. The optimization results show that LEO, with its acceptable ability to explore, exploit, and maintain a balance between them, has efficient performance in optimization applications and in solution presentation. LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms. Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior and more competitive in combating the compared algorithms.

“…Then, using the computational intelligence in the algorithm, the solutions are moved to the search space. This displacement is such that after passing through several iterations of the algorithm, the population converges toward the optimal point ( Dehghani & Trojovský, 2021 ). The strategy of changing the status of population members and moving them in the search space is the most important difference of optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Hubálovský

Trojovský

2022

PeerJ Computer Science

Self Cite

In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO’s performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO’s exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.