Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principle

Das, Sanjoy; Natarajan, Balasubramaniam; Stevens, Daniel; Koduru, Praveen

doi:10.1016/j.asoc.2007.05.012

Cited by 15 publications

(7 citation statements)

References 28 publications

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“…If the size of PP K is greater than n max1 , the individuals with small affinity are chosen to form C K Otherwise, let front= front+1, then use the same way to fill C K . d) Cloning operator [6] and immune operator. Through cloning each individual becomes the set of same individuals, then they form a new group BB K .…”

Section: B Description Of the Algorithmmentioning

confidence: 99%

“…The cloning principles [6] in MMGA are as follow: 1) Only those antibodies who can identify the antigens are amplified, but those who can not identify the antigens are not amplified. 2) A number of antibody cells with high affinity are elected to the pool of the memory.…”

Section: The Principle Of Immunity and Cloningmentioning

confidence: 99%

“…The main idea of MMGA is that the algorithm firstly generates initial even population, then initial population joints the external memory to re-form a new population which will be applied to the immune operation and cloning operation [6]. Immune operation and cloning operation are executed in the same generation.…”

Section: Mmga Algorithmmentioning

confidence: 99%

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Multi-objective Optimization of Power Control and Resource Allocation for Cognitive Wireless Networks

Bao

Jiang

Huang

et al. 2009

2009 Eighth IEEE/ACIS International Conference on Computer and Information Science

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Resource optimization is a very important aspect in cognitive radio network (CRN). It is a typical multiobjective optimization problem. This paper proposes a mixed multi-objective immune cloning genetic algorithm (MMGA) to solve the optimization of resource allocation in CRNs. Based on the genetic algorithm of non-domination sort, the MMGA adds external memory immune operator and cloning operator to effectively improve the searching performance. To evaluate the performance of MMGA, we compare it to NSGA-II with three typical test functions. From the results, the MMGA can solve multi-objective optimization problems more effectively than the NSGA-II. Simultaneously, the MMGA is used to optimize the frequency bandwidths and bandwidth-footprint product in CRNs. The simulation results show that MMGA can effectively solve the optimization of resource allocation in CRNs.

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Section: B Description Of the Algorithmmentioning

confidence: 99%

Section: The Principle Of Immunity and Cloningmentioning

confidence: 99%

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Multi-objective Optimization of Power Control and Resource Allocation for Cognitive Wireless Networks

Bao

Jiang

Huang

et al. 2009

2009 Eighth IEEE/ACIS International Conference on Computer and Information Science

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“…The CLONALG [8] inspired by the clonal selection principle [9] of the human IS is a newly discovered, high-performance evolutionary algorithm capable of solving general N -dimensional, linear and nonlinear optimization problems [8,[10][11][12][13][14][15]. The concept of the CLONALG is shown in Fig.…”

Section: Clonal Selection Algorithm (Clonalg)mentioning

confidence: 99%

“…It also has the ability of getting out local minima. As a relatively novel optimization algorithm, the CLONALG has been successfully applied to solve various engineering problems [8,[10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Clonal Selection Algorithm for Array Pattern Nulling by Controlling the Positions of Selected Elements

Babayiğit¹,

Güney²,

Akdagli³

2008

PIER B

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Abstract-In this paper, a method based on clonal selection algorithm (CLONALG) is proposed for null steering of linear antenna arrays by controlling only the positions of selected elements. The CLONALG is a relatively novel population-based evolutionary algorithm inspired by the clonal selection principle of the human immune system. In order to illustrate the accuracy and flexibility of the proposed algorithm, several numerical examples of Chebyshev pattern with the single and double nulls imposed at the directions of interference are given.

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Multiobjective Evolutionary Optimization

Biswas

Suganthan

2018

Wiley Encyclopedia of Electrical and Electronics Engineering

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Many computational techniques have been known for years to solve multiobjective optimization problems (MOPs). However, the nature ofMOPs has been changing and many more large‐scale multimodalMOPs, computationally expensiveMOPs, dynamicMOPs, noisyMOPs, and so on are introduced in multiobjective optimization domain. The researchers are thus inspired to look beyond the conventional approaches and focus more on evolutionary optimization techniques. The developments in the field of evolutionary algorithm (EA) in last few decades makeEAan effective tool to apply to complexMOPs. This article provides an overview of multiobjective evolutionary algorithms (MOEAs), different frameworks ofMOEAs, and the application ofMOEAs to variousMOPs. Performance indicators forMOEAs and some visualization methods in many‐objective optimization problems are also briefly mentioned in this article.

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Multi-objective and constrained optimization for DS-CDMA code design based on the clonal selection principle

Cited by 15 publications

References 28 publications

Multi-objective Optimization of Power Control and Resource Allocation for Cognitive Wireless Networks

Multi-objective Optimization of Power Control and Resource Allocation for Cognitive Wireless Networks

A Clonal Selection Algorithm for Array Pattern Nulling by Controlling the Positions of Selected Elements

Multiobjective Evolutionary Optimization

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