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

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 bes...

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Vydáno v:PeerJ. Computer science Ročník 8; s. e910
Hlavní autoři: Dehghani, Mohammad, Hubálovský, Štěpán, Trojovský, Pavel
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States PeerJ. Ltd 07.03.2022
PeerJ, Inc
PeerJ Inc
Témata:
Algorithm of best and worst members of the population
Algorithms
Algorithms and Analysis of Algorithms
Analysis
Birth rate
Design
Evolution & development
Exploitation
Genetic algorithms
Heuristic methods
Linear programming
Mathematical optimization
Mathematical programming
Optimization
Optimization algorithm
Optimization algorithms
Optimization problem
Optimization Theory and Computation
Performance evaluation
Pheromones
Population
Problem solving
Simulation
Subtraction
Teaching
Optimization algorithm
Algorithm of best and worst members of the population
Optimization
Optimization problem
ISSN:2376-5992, 2376-5992
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Shrnutí: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.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.910