A population-based algorithm with the selection of evaluation precision and size of the population

Cpałka, Krzysztof; Słowik, Adam; Łapa, Krystian

doi:10.1016/j.asoc.2021.108154

Cited by 7 publications

(5 citation statements)

References 69 publications

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“…Hybrid metaheuristic algorithms are designed to strike a balance between these two phases. Additionally, in metaheuristic algorithms, there is an important trade-off between convergence and accuracy [60]. A hybrid metaheuristic algorithm should aim to maintain a good balance between achieving accurate solutions and preserving diversity within the population, which helps prevent premature convergence.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Wang,

Ala,

Liu

et al. 2024

Journal of Artificial Intelligence and Soft Computing Research

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Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Wang,

Ala,

Liu

et al. 2024

Journal of Artificial Intelligence and Soft Computing Research

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“…In the case of offline parameter selection, they can be set by trial and error to static values, or values depending on a given step of the algorithm (see e.g. [11]). In the case of MPBAs, different parameters can be set for each population and thus increase the chance of adapting the algorithm to a given problem (see e.g.…”

Section: Parameter Adjustmentmentioning

confidence: 99%

“…by dynamically changing the number of populations or individuals, as well as a dynamic selection of surrogate solutions (see e.g. [11]).…”

Section: Adaptive Mechanismsmentioning

confidence: 99%

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A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Łapa,

Rutkowska,

Byrski

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method.

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“…As explored in the comprehensive analysis, nature-inspired algorithms draw from a broader spectrum of natural phenomena for problem-solving [3]. A nature-inspired population-based algorithm that dynamically adjusts its population size, selects specific modification operators for everyone, and controls the sampling period of optimized systems to simplify the fitness function and balance new solution searches with fine-tuning [4]. Within this domain, population-based algorithms stand out for their effectiveness in exploring vast search spaces [5].…”

Section: Introductionmentioning

confidence: 99%

Ant-Inspired Scheduling: Integrating Constraint Satisfaction Problem with Ant Colony Optimization

2024

IJATCSE

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The study integrated CSP with ACO to tackle complex scheduling challenges, demonstrating the robust capabilities of this approach. The results indicate that the integrated approach not only maintained high success rates across a range of constraints but also revealed the importance of precise parameter tuning in enhancing algorithm performance. Particularly, constraints that showed variations in success rates underlined the potential for further optimization to achieve consistent and effective outcomes. The effectiveness of the optimization algorithm was evaluated by measuring its success and performance rates. This approach proved to be a robust strategy for optimizing the study's problem structure, providing valuable insights into the dynamics of algorithmic performance.

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A population-based algorithm with the selection of evaluation precision and size of the population

Cited by 7 publications

References 69 publications

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Ant-Inspired Scheduling: Integrating Constraint Satisfaction Problem with Ant Colony Optimization

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