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Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms

COMPUTATIONAL optimization AND APPLICATIONS 2013年第2期56卷 457-480页

作者： Omran, Mahamed G. H. al-Sharhan, Salah Salman, Ayed Clerc, Maurice Gulf Univ Sci & Technol Dept Comp Sci Kuwait Kuwait Kuwait Univ Dept Comp Engn Kuwait Kuwait

In this paper, we investigate the use of low-discrepancy sequences to generate an initial population for population-based optimization algorithms. Previous studies have found that low-discrepancy sequences generally improve the performance of a population-based optimization algorithm. However, these studies generally have some major drawbacks like using a small set of biased problems and ignoring the use of non-parametric statistical tests. To address these shortcomings, we have used 19 functions (5 of them quasi-real-world problems), two popular low-discrepancy sequences and two well-known population-based optimization methods. According to our results, there is no evidence that using low-discrepancy sequences improves the performance of population-based search methods.

关键词： Low-discrepancy sequences Quasi-random sequences Pseudo-random sequences population-based optimization algorithms Particle swarm optimization Differential evolution

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Uncovering structural bias in population-based optimization algorithms: A theoretical and simulation-based analysis of the Generalized Signature Test

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Rajwar, Kanchan Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee India Indian Inst Technol Roorkee Mehta Family Sch Data Sci & Artificial Intelligenc Roorkee Uttarakhand India Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttrakhand India

population-based optimization algorithms are popular tools for optimization, influenced by two interactive factors: landscape bias, which guides the population toward better objective function values, and structural bias, which directs the population to a specific location in the search domain. Structural bias can impede the exploration of algorithms, compelling the population to revisit a particular area of the search domain, which leads to increased computing costs without yielding any new information. In this study, we present a comprehensive survey of structural bias. Identifying this bias in a general algorithm is challenging. We propose the 'Generalized Signature Test' as a methodology to detect and analyze the structural bias in population-based optimization algorithms. Numerical simulations are performed using six well-known metaheuristic algorithms: Genetic Algorithm, Differential Evolution, Particle Swarm optimization, Grey Wolf Optimizer, Whale optimization Algorithm, and Harris Hawks optimization. Experimental results suggest that Differential Evolution displays the least bias toward the center of the search domain, whereas the Harris Hawks optimization exhibits a significant axial-diagonal bias toward the origin. Our findings indicate that the proposed method is both efficient and straightforward for assessing algorithmic structural bias. The Generalized Signature Test provides an in-depth analysis and understanding of biases, tailor-made for thorough bias behavior investigations. The Generalized Signature Test can identify bias in established algorithms and evaluate bias in emerging algorithms at their early development stages.

关键词： population-based optimization algorithms Metaheuristic algorithms Genetic algorithm Structural bias Generalized signature test Theoretical analysis

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Ensemble strategies for population-based optimization algorithms - A survey

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 695-711页

作者： Wu, Guohua Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam Cent S Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China Kyungpook Natl Univ Sch Elect Engn Taegu 702701 South Korea Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In population-based optimization algorithms (POAs), given an optimization problem, the quality of the solutions depends heavily on the selection of algorithms, strategies and associated parameter combinations, constraint handling method, local search method, surrogate model, niching method, etc. In the literature, there exist several alternatives corresponding to each aspect of configuring a population-based algorithm such as one-point/two-points/uniform crossover operators, toumament/ranking/stochastic uniform sampling selection methods, Gaussian/Levy/Cauchy mutation operators, clearing/crowding/sharing based niching algorithms, adaptive penalty/epsilon/superiority of feasible constraint handling approaches, associated parameter values and so on. In POA literature, No Free Lunch (NFL) theorem has been well-documented and therefore, to effectively solve a given optimization problem, an appropriate configuration is necessary. But, the trial and error approach for the appropriate configuration may be impractical because at different stages of evolution, the most appropriate configurations could be different depending on the characteristics of the current search region for a given problem. Recently, the concept of incorporating ensemble strategies into POAs has become popular so that the process of configuring an optimization algorithm can benefit from both the availability of diverse approaches at different stages and alleviate the computationally intensive offline tuning. In addition, algorithmic components of different advantages could support one another during the optimization process, such that the ensemble of them could potentially result in a versatile POA. This paper provides a survey on the use of ensemble strategies in POAs. In addition, we also provide an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. and compare them with the ensemble strategies in the context of POAs.

关键词： Ensemble of algorithms No free lunch population-based optimization algorithms Numerical optimization Evolutionary algorithm Swarm intelligence Parameter/operator/strategy adaptation optimization algorithmic configuration adaptation Hyper-heuristics Island models Adaptive operator selection Multi-operator/multi-method approaches

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A color image compression and encryption algorithm combining compressed sensing, Sudoku matrix, and hyperchaotic map

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NONLINEAR DYNAMICS 2025年第3期113卷 2831-2865页

作者： Yao, Ming Chen, Zhong Deng, Hongwei Wu, Ximei Liu, Tongzhe Cao, Can Hengyang Normal Univ Coll Comp Sci & Technol Hengyang 421002 Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & App Hengyang 421002 Peoples R China Hengyang Normal Univ Coll Math & Stat Hengyang 421002 Peoples R China

In this paper, we propose a color image compression and encryption algorithm that combines compressed sensing, Sudoku matrix, and hyperchaotic map to ensure the security of image data and improve transmission and storage efficiency. Firstly, we design a novel two-dimensional sine-logistic coupled hyperchaotic map that exhibits a more continuous and broader chaotic range and more complex hyperchaotic behavior compared to some existing known chaotic maps. Secondly, to mitigate the impact of compression thresholds on image reconstruction quality, we employ multiple optimization strategies to improve the dung beetle optimization algorithm, enhancing its global exploration and local exploitation capabilities, thereby making it more efficient and accurate in handling complex optimization problems. based on this, we optimize the compression threshold using the improved algorithm to find the optimal threshold for the best image reconstruction quality. Finally, to further enhance the security of the algorithm, we introduce Sudoku matrices in the permutation stage. By using randomly generated Sudoku matrices to permute the image pixel positions, the original image structure is effectively disrupted, increasing the complexity and randomness of the encryption. In the diffusion stage, we employ a bidirectional diffusion operation to ensure that pixel information is effectively spread throughout the image, and we dynamically update the diffusion keys in each round, making the key sequence highly random and unpredictable. Experimental results demonstrate that the proposed algorithm can resist various illegal attacks and offers high security.

关键词： Image encryption Hyperchaotic map Compressed sensing population-based optimization algorithms Sudoku matrix

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A General Framework for Intelligent optimization algorithms based on Multilevel Evolutions 13th

A General Framework for Intelligent Optimization Algorithms ...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Wang, Chenchen Chen, Caifeng Lun, Ziru Ye, Zhanyu Liu, Qunfeng Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

Intelligent optimization is a kind of global optimization algorithms based on simulating biological intelligent behaviors such as evolution and foraging. Currently, there are numerous intelligent optimization algorithms have been proposed based on a large mount of animals' or plants' behaviors. This phenomenon shows the prosperity of this field, but bring issues about these algorithms' analysis and applications. We believe an extensive development stage has passed in the field of intelligent optimization, and more theoretical analysis and deep understanding about these algorithms become favorite. In this paper, we try to build a general framework for all population-based global optimization algorithms. This framework employs the idea of multilevel evolution, and therefore it can include not only the traditional bio-inspired evolution algorithms which often only evolute in a single level of search space, but also those population-based algorithms adopt data-driven strategies or cultural evolutions. By the help of the proposed framework, we can classify all population-based global optimization algorithms into three types, and improve the traditional algorithms. In this paper, this framework is then applied to the popular particle swarm optimization, and a modified particle swarm optimization with three-level of evolutions is proposed. Numerical results show that the modified algorithm improves the original one significantly.

关键词： Intelligent optimization algorithms population-based optimization algorithms General framework Multilevel evolutions

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Fitness Function Evaluations: A Fair Stopping Condition?

Fitness Function Evaluations: A Fair Stopping Condition?

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IEEE Symposium on Swarm Intelligence (SIS)

作者： Engelbrecht, A. P. Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

ISBN: (纸本)9781479944583

It has become acceptable practice to use only a limit on the number of fitness function evaluations (FEs) as a stopping condition when comparing population-based optimization algorithms, irrespective of the initial number of candidate solutions. This practice has been advocated in a number of competitions to compare the performance of population-based algorithms, and has been used in many articles that contain empirical comparisons of algorithms. This paper advocates the opinion that this practice does not result in fair comparisons, and provides an abundance of empirical evidence to support this claim. Empirical results are obtained from application of a standard global best particle swarm optimization (PSO) algorithm with different swarm sizes under the same FE computational limit, on a large benchmark suite.

关键词： algorithm theory particle swarm optimisation FE computational limit PSO algorithm benchmark suite candidate solutions fair stopping condition fitness function evaluations particle swarm optimization population-based optimization algorithms Algorithm design and analysis Benchmark testing Educational institutions Iron optimization Particle swarm optimization Standards

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Experimental evaluation of optimal tuning for PID parameters in an AVR system

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SCIENTIA IRANICA 2022年第6期29卷 3317-3325页

作者： Foroutan, F. Tootoonchian, F. Iran Univ Sci & Technol IUST Railway Engn Dept Tehran Iran Iran Univ Sci & Technol IUST Dept Elect Engn Tehran Iran

Automatic Voltage Regulator (AVR) is employed to stabilize the output voltage of generators at electric power plants. However, reliable performance of AVR depends on professional tuning of its PID controller parameters. Therefore, different optimization algorithms are used to determine those parameters. The objective of the optimization is defined as minimizing the characteristics of transient step response such as settling time, rise time, overshoot, and steady state error. Then, to verify the optimization results, a simulator is built experimentally for AVR and PID system which can also be used for other studies on AVR systems. Experimental results are compared with those of MATLAB and Pspice software. Close agreement between the simulation and experimental results confirms the success of the optimization. (c) 2022 Sharif University of Technology. All rights reserved.

关键词： Automatic Voltage Regulator (AVR) Proportional-Integral-Derivative (PID) controller population-based optimization algorithms Simulator Transient step response

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A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications

Franklin Open

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Franklin Open 2024年 8卷

作者： Hassan, Ibrahim Hayatu Abdullahi, Mohammed Isuwa, Jeremiah Yusuf, Sahabi Ali Aliyu, Ibrahim Tetengi Institute for Agricultural Research Ahmadu Bello University Zaria Nigeria Department of Computer Science Ahmadu Bello University Zaria Nigeria Computer Science Deparment Federal University Kashere Gombe State Nigeria

Metaheuristic algorithms are commonly used in solving complex and NP-hard optimization problems in various fields. These algorithms have become popular because of their ability to explore and exploit solutions in various problem domains. Honey Badger Algorithm (HBA) is a population-based metaheuristic optimization algorithm inspired by the dynamic hunting strategy of honey badgers, utilizing honey and digging-seeking techniques. Since its introduction in 2020, HBA has garnered widespread attention and has been applied across various domains. This review aims to comprehensively survey the improvement and application of HBA in solving various optimization problems. Additionally, the survey conducts a meta-analysis of the HBA's improvements, hybridization and application since its introduction. According to the result of the survey, 52 studies presented improved HBA using chaotic maps, levy flight mechanism, adaptive mechanisms, transfer functions, multi-objective mechanism and opposition based learning techniques, 20 studies presented a hybrid HBA with other metaheuristics algorithms and 101 studies uses the original HBA for solving various optimization problems. According to the survey, the wide acceptance of the HBA within the research community stems from its straightforwardness, ease of use, efficient computational time, accelerated convergence speed, high efficacy, and capability to address different kind of optimization issues, distinguishing it from the well-known optimization approches presented. © 2024

关键词： Honey badger algorithm Metaheuristic algorithms Nature-inspired optimization algorithm population-based optimization algorithms

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