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Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.


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Journal ArticleDOI
TL;DR: The aim of this work is to show the use of a well-known type of evolutionary computational optimization technique, ant colony optimization (ACO), in a typical electromagnetic problem: linear array synthesis.
Abstract: The aim of this work is to show the use of a well-known type of evolutionary computational optimization technique, ant colony optimization (ACO), in a typical electromagnetic problem: linear array synthesis. To this aim, an algorithm based on the fundamentals of ant colony optimization has been developed. The algorithm uses real numbers. Some examples using different optimization criteria are presented. Also, some guidelines for the use of the algorithm, especially for creating the desirability function, are supplied. The algorithm has been demonstrated to be versatile and useful for this problem. The purpose of the work is to show (via this particular application) the flexibility and easy implementation of this algorithm family, which makes it suitable for use in other electromagnetic optimization problems.

151 citations

Journal ArticleDOI
TL;DR: A hybrid optimization scheme for multiple thresholding by the criteria of Otsu's minimum within-group variance and Gaussian function fitting, which shows that the NM-PSO-Otsu could expedite the OTSu's method efficiently to a great extent in the case of multi-level thresholding, and that theNM- PSO-curve method could provide better effectiveness in the context of visualization, object size and image contrast.

151 citations

Journal ArticleDOI
TL;DR: The PSO–MADS hybrid procedure is shown to consistently outperform both stand-alone PSO and MADS when solving the joint problem, and is observed to provide superior performance relative to a sequential procedure.
Abstract: In oil field development, the optimal location for a new well depends on how it is to be operated. Thus, it is generally suboptimal to treat the well location and well control optimization problems separately. Rather, they should be considered simultaneously as a joint problem. In this work, we present noninvasive, derivative-free, easily parallelizable procedures to solve this joint optimization problem. Specifically, we consider Particle Swarm Optimization (PSO), a global stochastic search algorithm; Mesh Adaptive Direct Search (MADS), a local search procedure; and a hybrid PSO–MADS technique that combines the advantages of both methods. Nonlinear constraints are handled through use of filter-based treatments that seek to minimize both the objective function and constraint violation. We also introduce a formulation to determine the optimal number of wells, in addition to their locations and controls, by associating a binary variable (drill/do not drill) with each well. Example cases of varying complexity, which include bound constraints, nonlinear constraints, and the determination of the number of wells, are presented. The PSO–MADS hybrid procedure is shown to consistently outperform both stand-alone PSO and MADS when solving the joint problem. The joint approach is also observed to provide superior performance relative to a sequential procedure.

151 citations

Journal ArticleDOI
TL;DR: A new definition of Q-bit expression called quantum angle is proposed, and an improved particle swarm optimization (PSO) is employed to update the quantum angles automatically, and simulated results in solving 0-1 knapsack problem show that QSE is superior to traditional QEA.

151 citations

Journal ArticleDOI
TL;DR: The obtained results show that interesting classification performances can be achieved by the proposed methodology despite its completely unsupervised nature.
Abstract: In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of the class statistical parameters; 2) detection of the best discriminative bands without requiring the a priori setting of their number by the user; and 3) estimation of the number of data classes characterizing the considered image. It is formulated within a multiobjective particle swarm optimization (MOPSO) framework and is guided by three different optimization criteria, which are the log-likelihood function, the Bhattacharyya statistical distance between classes, and the minimum description length (MDL). A detailed experimental analysis was conducted on both simulated and real hyperspectral images. In general, the obtained results show that interesting classification performances can be achieved by the proposed methodology despite its completely unsupervised nature.

150 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023183
2022471
202110
20207
201926
2018171