A multimodal butterfly optimization using fitness-distance balance

Orujpour, Mohanna; Feizi-Derakhshi, Mohammad-Reza; Akan, Taymaz

doi:10.1007/s00500-023-09074-z

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…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%

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: Introductionmentioning

confidence: 99%

Multi-modal Battle Royale optimizer

Çiçek,

Akan,

Bayat

2024

Cluster Comput

Self Cite

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Parameter Optimization and Solution Performance Analysis of Multi-Modal Butterfly Optimization Algorithm

Lin,

Cheng

2024

IEEE Access

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Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem

Cai,

Qiao,

et al. 2024

Mathematics

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Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

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A multimodal butterfly optimization using fitness-distance balance

Cited by 3 publications

References 43 publications

Multi-modal Battle Royale optimizer

Multi-modal Battle Royale optimizer

Parameter Optimization and Solution Performance Analysis of Multi-Modal Butterfly Optimization Algorithm

Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem

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