Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation

Timmis, Jon; Edmonds, C.; Kelsey, J.

doi:10.1109/cec.2004.1330977

Cited by 36 publications

(21 citation statements)

References 12 publications

(23 reference statements)

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“…For comparison, the numerical experiments were performed with six algorithms by results of average and best for Standard Artificial Immune System algorithm as SAIS [23], B-Cell Algorithm as BCA [24], Clonal Selection Algorithm as CSA [25], Adaptive Clonal Selection Algorithm as ACSA [26], Optimization Artificial Immune Network as OAIN [27], Standard Genetic Algorithm as SGA [4], Artificial ImmuneGenetic Algorithm as AIGA [18], and finally proposed method in algorithm 1, Learning Automata-based Artificial Immune System as AISLA and algorithm 2, Learning Automata-based Cooperative Artificial Immune System as CAISLA.…”

Section: Resultsmentioning

confidence: 99%

LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

Rezvanian

Meybodi

2010

Communications in Computer and Information Science

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Abstract. Artificial Immune System (AIS) is taken into account from evolutionary algorithms that have been inspired from defensive mechanism of complex natural immune system. For using this algorithm like other evolutionary algorithms, it should be regulated many parameters, which usually they confront researchers with difficulties. Also another weakness of AIS especially in multimodal problems is trapping in local minima. In basic method, mutation rate changes as only and most important factor results in convergence rate changes and falling in local optima. This paper presented two hybrid algorithm using learning automata to improve the performance of AIS. In the first algorithm entitled LA-AIS has been used one learning automata for tuning the hypermutation rate of AIS and also creating a balance between the process of global and local search. In the second algorithm entitled LA-CAIS has been used two learning automata for cooperative antibodies in the evolution process. Experimental results on several standard functions have shown that the two proposed method are superior to some AIS versions.

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Section: Resultsmentioning

confidence: 99%

LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

Rezvanian

Meybodi

2010

Communications in Computer and Information Science

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“…Comparative tests between opt-AINet and other algorithms have been performed in [7]. The reported results show that, while the algorithm was successful in finding the global optimum of the test functions used, the number of function evaluations needed was much bigger than that for the other algorithms used for comparison.…”

Section: Previous Workmentioning

confidence: 97%

“…This algorithm was inspired by the idiotypic network theory for explaining the immune system dynamics, originally proposed in [5]. The optimization version of the AINet [6], [7] is called opt-AINet, and presents a number of interesting features, such as dynamic variation of the population size, local and global search, and the ability to maintain any number of optima. These are highly desirable characteristics, but they are obtained at the cost of a very large number of objective function evaluations.…”

Section: Introductionmentioning

confidence: 99%

A modified immune network algorithm for multimodal electromagnetic problems

et al. 2006

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Some optimization algorithms based on theories from immunology have the feature of finding an arbitrary number of optima, including the global solution. However, this advantage comes at the cost of a large number of objective function evaluations, in most cases, prohibitive in electromagnetic design. This paper proposes a modified version of the artificial immune network algorithm (opt-AINet) for electromagnetic design optimization. The objective of this modified AINet (m-AINet) is to reduce the computational effort required by the algorithm, while keeping or improving the convergence characteristics. Another improvement proposed is to make it more suitable for constrained problems through the utilization of a specific constraint-handling technique. The results obtained over an analytical problem and the design of an electromagnetic device show the applicability of the proposed algorithm.Index Terms-Artificial immune systems, electromagnetic design optimization, immune networks.

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“…In this algorithm, frogs are seen as hosts for memes and described as a memetic vector. Each meme consists of a number of memo types [13]. The memo types represent an idea in a manner similar to a gene representing a trait in a chromosome in a genetic algorithm.…”

Section: Proposed Work 21 Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering

Chittineni¹,

Reddy²,

Satapathy³

2016

IJCA

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Data clustering is one of the data mining task, it is used to group the data objects according to their similarity. It is an optimization problem to find optimal results apply the proposed parallel approach called P-AISFLA. This hybrid algorithm is developed by utilizing the benefits of both social and immune mechanisms. The social algorithm Shuffled Frog Leaping Algorithm is a new parameter free population based algorithm combined with Clonal selection algorithm CSA.This hybrid algorithm performs the parallel computation of social behavior based SFLA and Immune behavior based CSA to improve the ability to reach the global optimal solution with a faster and a rapid convergence rate.The proposed algorithm PAISFL is applied for the data clustering applications and proved that it produces optimal results than SFLA and PSO.

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Assessing the performance of two immune inspired algorithms and a hybrid genetic algorithm for function optimisation

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 36 publications

References 12 publications

LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

LACAIS: Learning Automata Based Cooperative Artificial Immune System for Function Optimization

A modified immune network algorithm for multimodal electromagnetic problems

A Novel Immune Optimization with Shuffled Frog Leaping Algorithm - A Parallel Approach for Unsupervised Data Clustering

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