A memetic animal migration optimizer for multimodal optimization

Farshi, Taymaz Rahkar

doi:10.1007/s12530-021-09368-3

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…MBRO and its competitors (CFA [20], EPSO [18], FERPSO [6], LSPSO [19], NGSA [37], MFPA [39], MAMO, and [22]) have undergone testing on a set of 14 widely employed multi-modal benchmark functions from CEC 2013 and CEC 2015, as referenced in [26] [27]. The different features of these functions are listed in Table 1.…”

Section: Test Functionmentioning

confidence: 99%

“…These adaptations aim to enable the exploration of multiple optima in the search space. Here are some examples of optimization algorithms that have been extended for multimodal optimization: Niche Gravitational Search Algorithm (NGSA) [20], Multimodal Firefly Algorithm [20], Multimodal Flower Pollination Algorithm [21],Multimodal Animal Migration Optimization Algorithm (AMO) [22], Multimodal Bacterial Foraging Optimization (MBF0) [23], Multimodal Butterfly Optimization Using Fitness-Distance Balance [24] and etc.…”

Section: Introductionmentioning

confidence: 99%

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Multi-modal Battle Royale optimizer

Çiçek,

Akan,

Bayat

2024

Cluster Comput

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Multimodal optimization poses a challenging problem in the field of optimization as it entails the discovery of multiple local and global optima, unlike unimodal optimization, which seeks a single global solution. In recent years, the significance of addressing multimodal optimization challenges has grown due to the real-world complexity of many problems. While numerous optimization methods are available for unimodal problems, multimodal optimization techniques have garnered increased attention. However, these approaches often grapple with a common issue: the determination of the niching parameter, necessitating prior knowledge of the problem space. This paper introduces a novel multimodal optimization approach that circumvents the need for prior problem space knowledge and avoids the challenge of predefining the niching parameter. Building upon the Battle Royal Optimization (BRO) algorithm, this extended version formulates a multimodal solution by utilizing Coulomb's law to identify suitable neighbors. The incorporation of Coulomb's law serves the dual purpose of identifying potential local and global optima based on fitness values and establishing optimal distances from solution candidates. A comparison study was done between the MBRO and seven well-known multimodal optimization algorithms using 14 benchmark problems from the CEC 2013 and CEC 2015 competitions to see how well it worked. The experimental results underscore MBRO's proficiency in successfully identifying most, if not all, local and global optima, positioning it as a superior solution when compared to its competitors.

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Section: Test Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-modal Battle Royale optimizer

Çiçek,

Akan,

Bayat

2024

Cluster Comput

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“…The proposed NSPSO is designed to deal with RMOPs such as the finding of LSF which can also be seen as a special multimodal optimization problem. Therefore four-NMMSO [31], RSCMSAESII [25], Multi_AMO [32] and DP-MSCC-ES [33]-are chosen as competitive algorithms for the comparative study. Notably, the RS-CMSA-ESII won the championship in the GECCO 2020 Competition on Niching Methods for Multimodal Optimization.…”

Section: Comparative Analysis Settingsmentioning

confidence: 99%

A Novel Sampling Method Based on Normal Search Particle Swarm Optimization for Active Learning Reliability Analysis

Yuan

et al. 2023

Applied Sciences

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In active learning reliability methods, an approximation of limit state function (LSF) with high precision is the key to accurately calculating the failure probability (Pf). However, existing sampling methods cannot guarantee that candidate samples can approach the LSF actively, which lowers the accuracy and stability of the results and causes excess computational effort. In this paper, a novel candidate samples-generating algorithm was proposed, by which a group of evenly distributed candidate points on the predicted LSF of performance function (either the real one or the surrogate model) could be obtained. In the proposed method, determination of LSF is considered as an optimization problem in which the absolute value of performance function was considered as objective function. After this, a normal search particle swarm optimization (NSPSO) was designed to deal with such problems, which consists of a normal search pattern and a multi-strategy framework that ensures the uniform distribution and diversity of the solution that intends to cover the optimal region. Four explicit performance functions and two engineering cases were employed to verify the effectiveness and accuracy of NSPSO sampling method. Four state-of-the-art multi-modal optimization algorithms were used as competitive methods. Analysis results show that the proposed method outperformed all competitive methods and can provide candidate samples that evenly distributed on the LSF.

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