Performance analysis of selected metaheuristic optimization algorithms applied in the solution of an unconstrained task

Knypiński, Łukasz

doi:10.1108/compel-07-2021-0254

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…The Particle Swarm Optimization (PSO) algorithm evaluates the quality of each particle through the fitness function. Each iteration of particles will update their relative positions [11], simulate the birds flying foraging behavior, and collaborate collectively to find optimal solutions [15]. The traditional continuous optimality search rule [14] is as follows：…”

Section: Research On Dynamic Failure Rate Finding Methodsmentioning

confidence: 99%

Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate

Gu¹,

Nie²,

Han³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In this paper, a dynamic preventive maintenance strategy is proposed for the problem of high maintenance cost rate due to excessive maintenance caused by unreasonable maintenance threshold setting when complex electromechanical equipment maintenance strategy is formulated. Increasing failure rate factor and decreasing service age factor are introduced to describe the evolution rules of failure rate during the maintenance of the coating machine, and the BP-LSTM (BP-Long Short Term Memory Network, BP-LSTM) model is combined to predict the failure rate of the coating machine. A Dynamic preventive maintenance Model (DM) that relies on dynamic failure rate thresholds to classify the three preventive maintenance modes of minor, medium and major repairs is constructed. A dynamic preventive maintenance strategy optimization process based on Genetic-Particle Swarm Optimization (GPSO) algorithm with the lowest cost rate per unit time in service phase is built to solve the difficult problem of dynamic failure rate threshold finding. Based on the historical operating data of the coating machine, a case study of the dynamic preventive maintenance strategy of the coating machine was conducted to verify the effectiveness of the model and the developed maintenance strategy proposed in this paper. The results show that the maintenance strategy developed in this paper can ensure better economy and applicability.

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Section: Research On Dynamic Failure Rate Finding Methodsmentioning

confidence: 99%

Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate

Gu¹,

Nie²,

Han³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…In order to compare advantages of the hybrid CS (HCS) being developed with other heuristic algorithms, the calculation has been made for following algorithms: (a) genetic algorithms (GA), (b) particle swarm optimization (PSO), and (c) bat algorithm (BA) [32]. The calculations have been executed for the Himmelblau function.…”

Section: Hybrid Cs Versus Classical Csmentioning

confidence: 99%

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

Knypiński

2023

Bulletin of the Polish Academy of Sciences Technical Sciences

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The paper presents a novel hybrid cuckoo search (CS) algorithm for the optimization of the line-start permanent magnet synchronous motor (LSPMSM). The hybrid optimization algorithm developed is a merger of the heuristic algorithm with the deterministic Hooke-Jeeves method. The hybrid optimization procedure developed was tested on analytical benchmark functions and the results were compared with the classical cuckoo search algorithm, genetic algorithm, particle swarm algorithm and bat algorithm. The optimization script containing a hybrid algorithm was developed in Delphi Tiburón. The results presented show that the modified method is characterized by better accuracy. The optimization procedure developed is related to a mathematical model of the LSPMSM. The multi-objective compromise function was applied as an optimality criterion. Selected results were presented and discussed.

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“…The MH optimization algorithms offer a cost-effective, straightforward, and efficient approach to addressing such difficulties. Optimal or near-optimal solutions can be obtained within a relatively brief timeframe [10][11][12]. The algorithm can identify the most effective approach for each problem instance and obtain the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Kailasam,

Nalliah,

Nallagoundanpalayam Muthusamy

et al. 2023

Biomimetics

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In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.

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Performance analysis of selected metaheuristic optimization algorithms applied in the solution of an unconstrained task

Cited by 14 publications

References 29 publications

Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate

Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

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