Developing Niching Algorithms in Particle Swarm Optimization

Li, Xiaodong

doi:10.1007/978-3-642-17390-5_3

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…In traditional mathematical optimization, the sequential niching method is a technique to efficiently locate multiple optimal solutions in multimodal problems [44]. It means that once a potential solution is identified by a specific optimization algorithm, the objective function's fitness landscape will be modified by a derating function at the identified position and the algorithm will be restarted to search for the second solution without converging to the previous one.…”

Section: B Sequential Targeting Strategymentioning

confidence: 99%

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Shi

Yan

Xiong

et al. 2021

IEEE Trans.on Nanobioscience

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We have proposed a new tumor sensitization and targeting (TST) framework, named in vivo computation, in our previous investigations. The problem of TST for an early and microscopic tumor is interpreted from the computational perspective with nanorobots being the "natural" computing agents, the high-risk tissue being the search space, the tumor targeted being the global optimal solution, and the tumortriggered biological gradient field (BGF) providing the aided knowledge for fitness evaluation of nanorobots. This natural computation process can be seen as on-the-fly path planning for nanorobot swarms with an unknown target position, which is different from the traditional path planning methods. Our previous works are focusing on the TST for a solitary lesion, where we proposed the weak priority evolution strategy (WP-ES) to adapt to the actuating mode of the homogeneous magnetic field used in the state-of-the-art nanorobotic platforms, and some in vitro validations were performed. In this paper, we focus on the problem of TST for multifocal tumors, which can be seen as a multimodal optimization problem for the "natural" computation. To overcome this issue, we propose a sequential targeting strategy (Se-TS) to complete TST for the multiple lesions with the assistance of nanorobot swarms, which are maneuvered by the external actuating and tracking devices according to the WP-ES. The Se-TS is used to modify the BGF landscape after a tumor is detected by a nanorobot swarm with the gathered BGF information around the detected tumor. Next, another nanorobot swarm will be employed to find the second tumor according to the modified BGF landscape without being misguided to the previous one. In this way, all the tumor lesions will be detected one by one. In other words, the paths of nanorobots to find the targets can be generated successively with the sequential modification of the BGF landscape. To demonstrate the effectiveness of the proposed Se-TS, we perform comprehensive simulation studies by enhancing the WP-ES based swarm intelligence algorithms using this strategy considering the realistic in-body constraints. The performance is compared against that of the "brute-force" search, which corresponds to the traditional systemic tumor targeting, and also against that of the standard swarm intelligence algorithms from the algorithmic perspective. Furthermore, some in vitro experiments are performed by using Janus microparticles as

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Section: B Sequential Targeting Strategymentioning

confidence: 99%

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Shi

Yan

Xiong

et al. 2021

IEEE Trans.on Nanobioscience

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“…To achieve this goal, it is important to improve the population diversity and different techniques have been proposed in the literature [8,42] . The second type focuses on the niching methods [19,32] . The key idea is to identify multiple optima using a group of niches and then maintain the identified optima till the end of the algorithm.…”

Section: Related Work On Pso For Mmopmentioning

confidence: 99%

“…In recent years, niching PSOs are commonly used for solving MMOPs, and a number of niching PSOs have been proposed, such as VPSO [37] and nichePSO [3] . Niching PSOs search for the optima with the help of multiple stable subpopulations [19] . Due to its advantages, niching PSO has become one of the most popular approaches for MMOPs and has also been applied to a range of practical applications, such as clustering problems [26] and inversion teleseismic waves [14] .…”

Section: Introductionmentioning

confidence: 99%

Niching particle swarm optimization with equilibrium factor for multi-modal optimization

Chen

Zhong

et al. 2019

Information Sciences

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Multi-modal optimization is an active research topic that has attracted increasing attention from evolutionary computation community. Particle swarm optimization (PSO) with niching technique is one of the most effective approaches for multi-modal optimization. However, in existing PSO with niching methods, the number of particles around different niches varies distinctly from each other, which makes it difficult for the algorithm to find high-quality solutions in all niches. To address this issue, this paper proposes a new niching PSO with equilibrium factor named E-SPSO. Different from the existing niching PSOs, the numbers of particles in different niches have been kept in balance in E-SPSO. The velocity of each particle is influenced by not only the personal best particle and the global best particle, but also an equilibrium factor (EF). By using the equilibrium factor to update the velocities of particles, the particles can be allocated uniformly among the niches. In this way, the computation resources can be assigned to the niches in a more balanced manner, so that the algorithm can gain more population diversity and find high-quality solutions in all niches. Experimental results on eleven benchmark problems show that the proposed mechanism not only increases the number of optima found, but also improves the search efficiency.

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“…These optima could be both local or global. The objective of multimodal optimisation techniques is to find as many of these optima as the designer might find interesting and satisfying for a particular problem [3,4].…”

Section: Introductionmentioning

confidence: 99%

Diversity Measures for Niching Algorithms

Mwaura

Engelbrecht

Nepomuceno³

2021

Algorithms

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Multimodal problems are single objective optimisation problems with multiple local and global optima. The objective of multimodal optimisation is to locate all or most of the optima. Niching algorithms are the techniques utilised to locate these optima. A critical factor in determining the success of niching algorithms is how well the search space is covered by the candidate solutions. For niching algorithms, high diversity during the exploration phase will facilitate location and identification of many solutions while a low diversity means that the candidate solutions are clustered at optima. This paper provides a review of measures used to quantify diversity, and how they can be utilised to quantify the dispersion of both the candidate solutions and the solutions of niching algorithms (i.e., found optima). The investigated diversity measures are then used to evaluate the distribution of candidate solutions and solutions when the enhanced species-based particle swarm optimisation (ESPSO) algorithm is utilised to optimise a selected set of multimodal problems.

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Developing Niching Algorithms in Particle Swarm Optimization

Cited by 10 publications

References 26 publications

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Nanorobots-Assisted Natural Computation for Multifocal Tumor Sensitization and Targeting

Niching particle swarm optimization with equilibrium factor for multi-modal optimization

Diversity Measures for Niching Algorithms

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