Evolutionary Multimodal Optimization

Multimodal Optimization As A Clustering Task: General Formulation And Application In The Context Of Particle Swarm

10.21528/cbic2011-10.5 ◽

2016 ◽

Author(s):

Paulo S. Prampero ◽

Levy Boccato ◽

Romis Attux

Keyword(s):

Particle Swarm ◽

Multimodal Optimization

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A development on multimodal optimization technique and its application in structural damage detection

Applied Soft Computing ◽

10.1016/j.asoc.2020.106264 ◽

2020 ◽

Vol 91 ◽

pp. 106264

Author(s):

Diancheng Chen ◽

Yiyang Li

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Optimization Technique ◽

Multimodal Optimization ◽

Structural Damage Detection

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A Modified Niching Crow Search Approach to Well Placement Optimization

Energies ◽

10.3390/en14040857 ◽

2021 ◽

Vol 14 (4) ◽

pp. 857

Author(s):

Jahedul Islam ◽

Md Shokor A. Rahaman ◽

Pandian M. Vasant ◽

Berihun Mamo Negash ◽

Ahshanul Hoqe ◽

...

Keyword(s):

Optimization Problem ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Multimodal Optimization ◽

Test Functions ◽

Well Placement ◽

Suggested Approach ◽

Placement Optimization ◽

Well Placement Optimization ◽

Search Approach

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

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A Novel Parametric Benchmark Generator for Dynamic Multimodal Optimization

Swarm and Evolutionary Computation ◽

10.1016/j.swevo.2021.100924 ◽

2021 ◽

pp. 100924

Author(s):

Ali Ahrari ◽

Saber Elsayed ◽

Ruhul Sarker ◽

Daryl Essam ◽

Carlos A. Coello Coello

Keyword(s):

Multimodal Optimization

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Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using A Clustering-Genetic-Algorithm-Based Approach

Applied Sciences ◽

10.3390/app11083388 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3388

Author(s):

Pan Zou ◽

Manik Rajora ◽

Steven Y. Liang

Keyword(s):

Genetic Algorithm ◽

Clustering Algorithm ◽

Flow Shop ◽

Optimal Schedule ◽

Flow Shop Scheduling ◽

Multimodal Optimization ◽

Shop Scheduling ◽

Permutation Flow Shop Scheduling ◽

Permutation Flow Shop ◽

Global Optima

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

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Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization

Remote Sensing ◽

10.3390/rs13040594 ◽

2021 ◽

Vol 13 (4) ◽

pp. 594

Author(s):

Rui Wang ◽

Yiming Zhang ◽

Weiming Tian ◽

Jiong Cai ◽

Cheng Hu ◽

...

Keyword(s):

Target Detection ◽

Computational Cost ◽

Detection Performance ◽

Coherent Detection ◽

Observation Data ◽

Multimodal Optimization ◽

Performance Simulation ◽

Single Target ◽

Parameter Search ◽

Long Time

Entomological radars are important for scientific research of insect migration and early warning of migratory pests. However, insects are hard to detect because of their tiny size and highly maneuvering trajectory. Generalized Radon–Fourier transform (GRFT) has been proposed for effective weak maneuvering target detection by long-time coherent detection via jointly motion parameter search, but the heavy computational burden makes it impractical in real signal processing. Particle swarm optimization (PSO) has been used to achieve GRFT detection by fast heuristic parameter search, but it suffers from obvious detection probability loss and is only suitable for single target detection. In this paper, we convert the realization of GRFT into a multimodal optimization problem for insect multi-target detection. A novel niching method without radius parameter is proposed to detect unevenly distributed insect targets. Species reset and boundary constraint strategy are used to improve the detection performance. Simulation analyses of detection performance and computational cost are given to prove the effectiveness of the proposed method. Furthermore, real observation data acquired from a Ku-band entomological radar is used to test this method. The results show that it has better performance on detected target amount and track continuity in insect multi-target detection.

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