An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems

Gao, Shangce; Tang, Zheng; Dai, Hongwei; Zhang, Jianchen

doi:10.1093/ietfec/e90-a.12.2930

Cited by 26 publications

(28 citation statements)

References 30 publications

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“…In our previous work [18], we had demonstrated that an equivalent level of P hm : P re , that is, λ = 0.5 will lead the clonal selection algorithm to a better performance. After this step, we obtain p i mutated cells just as (EP 1,1 , EP 1,2 , .…”

Section: Quantum Interference Crossover-based Clonal Selection Algorithmmentioning

confidence: 99%

“…5. Four algorithms CSA, RECSA, CQCCSA, and IQCCSA mean Classical Clonal Selection algorithm [12], improved clonal selection theory by considering Receptor Editing [18], Classical Quantum Crossover-based RECSA, and Improved Quantum Crossover-based RECSA respectively.…”

Section: Diversity Vs Balance Of Exploration and Exploitationmentioning

confidence: 99%

“…This emerging field of research is known as Artificial Immune System (AIS) [11]. Based on the different immune theories, various algorithms such as Danger Theory (DT) models [2], [3], Negative Selection Algorithms [5], [17], Immune Network Theory-based model [15], Clonal Selection Algorithms (CSA) [12], [18] are proposed and also applied on patter recognition [9],intrusion detection [10], [21], optimization problems [1], [14] and so on.…”

Section: Introductionmentioning

confidence: 99%

“…That is, the random hypermutation is little effective to improve the affinity. To overcome the low efficiency problem of the random hypermutation, a novel clonal selection algorithm [18] has been proposed by considering the Receptor Editing (RE) mechanisms. The receptor editing operator allows the immune system to rescue the low affinity immune cells before deletion.…”

Section: Introductionmentioning

confidence: 99%

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Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem

Dai

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYClonal Selection Algorithm (CSA), based on the clonal selection theory proposed by Burnet, has gained much attention and wide applications during the last decade. However, the proliferation process in the case of immune cells is asexual. That is, there is no information exchange during different immune cells. As a result the traditional CSA is often not satisfactory and is easy to be trapped in local optima so as to be premature convergence. To solve such a problem, inspired by the quantum interference mechanics, an improved quantum crossover operator is introduced and embedded in the traditional CSA. Simulation results based on the traveling salesman problems (TSP) have demonstrated the effectiveness of the quantum crossover-based Clonal Selection Algorithm. key words: clonal selection algorithm, quantum interference crossover, traveling salesman problem, hybrid model

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Section: Quantum Interference Crossover-based Clonal Selection Algorithmmentioning

confidence: 99%

Section: Diversity Vs Balance Of Exploration and Exploitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem

Dai

et al. 2009

IEICE Trans. Inf. & Syst.

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“…To address such problems, bioinspired intelligence algorithms (Da Silva Santos et al, 2010;Gao, 2012) have attracted more and more interest, among which the immunological algorithm (IA) is a particular class of optimization methods inspired by the basic features of adaptive immune response to antigenic stimulus. Most IAs mimic the metaphors of clonal selection principle (de Castro and Zuben, 2002), hypermutation (Freitas and Timmis, 2007), receptor editing (Gao et al, 2007) and lateral interaction effect (Whitbrook et al, 2007), providing a promising search mechanism by exploiting and exploring the solution space in parallel and effectively (Dasgupta et al, 2011). The main unique property of IAs is the utilization of the clonal proliferation, and the clonal selection which returns promising solutions acquired in the learning process.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

Gao¹,

Gong²,

Zhong³

et al. 2013

Advances in Artificial Life, ECAL 2013

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Immunological algorithms are a kind of bio-inspired intelligence methods which draw inspiration from natural immune systems. The problem-solving performance of immunological algorithms mainly lies on the utilization of learning (i.e. mutation) operators. In this paper, nine different learning operators in a standard immune algorithmic framework are investigated. These learning operators consist of eight existing operators and a newly proposed search direction based operator. Experiments are conducted based on nine variants of immunological algorithms that use different learning operators. Simulation results on a large number of benchmark optimization problems give a deep insight into the characteristics of these operators, and further verify that the proposed new learning operator can greatly improve the performance of immunological algorithms.

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A chaotic immune algorithm with fuzzy adaptive parameters

Qian

2008

Asia-Pacific J Chem Eng

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Adversity loss of the population usually leads to premature convergence problem of the algorithm. To deal with this problem, this paper presents a chaotic hypermutation immune algorithm (CHIA), which is built upon clonal selection principle and immune network regulatory mechanism. In CHIA, a bidirection adaptive chaotic hypermutation is introduced to create antibodies of a new genetic type. Simultaneously, a new immune network regulatory strategy based on the stimulation level of the antibody is devised to maintain current population adversity. Intensive comparison between CHIA and clonal selection algorithm (CLONALG) on function optimization is carried out and the simulation results show that CHIA can overcome the premature convergence problem in CLONALG. Additionally, parameter setting is a crucial and time-consuming task in the algorithm design. Through the parameter analysis of CHIA, two fuzzy modules are constructed to tune hypermutation factor and death ratio automatically, which eventually form an improved fuzzy adaptive chaotic immune algorithm (FACIA). According to the encouraging results obtained, it can be concluded that FACIA can significantly enhance the adaptability and improve the convergent performance of CHIA through the application of fuzzy logic in parameter setting. 

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An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems

Cited by 26 publications

References 30 publications

Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem

Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem

Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

A chaotic immune algorithm with fuzzy adaptive parameters

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