A clonal selection algorithm for optimization in electromagnetics

Campelo, Felipe; Guimarães, Frederico Gadelha; Igarashi, Hajime; Ramírez, J.A.

doi:10.1109/tmag.2005.846043

Cited by 107 publications

(49 citation statements)

References 7 publications

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“…It allocates the memory unit antibody population randomly Ying Yu et al [10] (2004) Proposed the use of learning operator in the clonal selection algorithm. This learning operator is not only increase the learning but also increase the detection efficiency Felipe Campelo et al [11] …”

Section: Referencementioning

confidence: 99%

Artificial Immune System: State of the Art Approach

Mishra¹,

Bhusry²

2015

IJCA

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The inspiration of framing the artificially developed immune system (AIS) is done through the biological immune system which compromise of signified information processing and self-adapting system. Since it originated in the 1990s, the branch of AIS gets a significant success in the field of Computational Intelligence. Present paper insights major works in the area of AIS and explore current advancements in applied system since past years. It has been observed that the particular research focused on three major considerable algorithms of AIS: (1) clonal selection algorithms (2) negative selection algorithm (3) artificial immune networks. However, computer scientists and engineers are motivated by the biological immune system to evolve new models and problem solving approaches. Developed AIS applications in extensive amount have received a lot of researcher's attention who were planning to establish models based on immune system and techniques in order to provide solutions for complicated problems of engineering. This paper presents a survey of current models of AIS and its algorithms.

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Section: Referencementioning

confidence: 99%

Artificial Immune System: State of the Art Approach

Mishra¹,

Bhusry²

2015

IJCA

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“…Clone selection algorithm is one of the many branches of the artificial immune system algorithms that has recently drawn considerable attention in a wide range of applications [7]. It has been shown that the clone selection algorithm is very effective on multimodal problems due to its inherent properties [8]. A real-valued parallel clone selection algorithm was successfully applied in optimization of multi-layered planar FSS [9].…”

Section: Introductionmentioning

confidence: 99%

Efficient Multi-Objective Optimization of Frequency Selective Radome With Nonuniform Wall Thickness

Ma¹,

Wan²

2014

PIER M

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Abstract-An efficient optimization technique for frequency selective surface (FSS) radome with nonuniform wall thickness is proposed to improve the power transmission efficiency and the boresight error (BSE) of FSS radome simultaneously. The high-frequency method based on the approximate locally planar technique is used to evaluate the transmission performance of FSS radome. An efficient multi-dimensional adaptive sampling method combined with spectral domain method of moment (MoM) is employed to analyze transmission performance of FSS structure. The immune clone algorithm (ICA) is applied to the design of a FSS radome, in which the linear combination of the maximizing power transmission efficiency and the minimizing BSE is adopted as the affinity function, and the radome wall thickness is optimized. A design example for the three-dimensional tangent ogive radome with nonuniform thickness is given. The results show that the power transmission efficiency is improved significantly and the BSE of the optimal antenna-radome system is also reduced over the antenna scan volume.

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“…Therefore, this type of algorithms have a widely range of applications, especially suitable for processing complex and nonlinear problems, which can not be solved by traditional methods easily. At present, as a kind of highly efficient methods, the bio-inspired optimization algorithms have been widely applied to real various optimization problem of real world [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Lifecycle-based Swarm Optimization Method for Constrained Optimization

Shen¹,

Zhu²,

Jin³

et al. 2011

JCP

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Abstract-Each biologic must go through a process from birth, growth, reproduction until death, this process known as life cycle. This paper borrows the biologic life cycle theory to propose a Lifecycle-based Swarm Optimization (LSO) algorithm. Based on some features of life cycle, LSO designs six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In this paper, the capability of the LSO to address constrained optimization problem was investigated. Firstly, the proposed method was test on some well-known and widely used benchmark problems. When compared with PSO, we can see that LSO can obtain the better solution and lower standard deviation than PSO on many different types of constrained optimization problems. Finally, LSO was also used for seeking the optimal route for vehicle route problem in logistics system. The result of LSO is the best when comparing with PSO and GA. The results of above two types of experiments, which include not only the ordinary benchmark problem but also the practical problems in engineering, demonstrate that LSO is a competitive and effective approach for solving constrained problems.

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A clonal selection algorithm for optimization in electromagnetics

Cited by 107 publications

References 7 publications

Artificial Immune System: State of the Art Approach

Artificial Immune System: State of the Art Approach

Efficient Multi-Objective Optimization of Frequency Selective Radome With Nonuniform Wall Thickness

Lifecycle-based Swarm Optimization Method for Constrained Optimization

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