Design of Fixed and Ladder Mutation Factor-Based Clonal Selection Algorithm for Solving Unimodal and Multimodal Functions | Zendy

Suresh Chittineni | Zendy; Arun Pradeep | Zendy; Dinesh Godavarthi | Zendy; Suresh Chandra Satapathy | Zendy; S. Mohan Krishna | Zendy; P. V. G. D. Prasad Reddy | Zendy

Open Access

Design of Fixed and Ladder Mutation Factor-Based Clonal Selection Algorithm for Solving Unimodal and Multimodal Functions

Author(s) -

Suresh Chittineni,

Arun Pradeep,

Dinesh Godavarthi,

Suresh Chandra Satapathy,

S. Mohan Krishna,

P. V. G. D. Prasad Reddy

Publication year - 2011

Publication title -

applied computational intelligence and soft computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.371

H-Index - 10

eISSN - 1687-9732

pISSN - 1687-9724

DOI - 10.1155/2011/210918

Subject(s) - clonal selection algorithm , clonal selection , mutation , benchmark (surveying) , computer science , selection (genetic algorithm) , factor (programming language) , algorithm , stability (learning theory) , convergence (economics) , artificial immune system , artificial intelligence , machine learning , genetics , biology , geodesy , economic growth , gene , economics , immunology , programming language , geography

Clonal selection algorithms (CSAs) is a special class of immune algorithms (IA), inspired by the clonal selection principle of the human immune system. To improve the algorithm's ability to perform better, this CSA has been modified by implementing two new concepts called fixed mutation factor and ladder mutation factor. Fixed mutation factor maintains a constant factor throughout the process, where as ladder mutation factor changes adaptively based on the affinity of antibodies. This paper compared the conventional CLONALG, with the two proposed approaches and tested on several standard benchmark functions. Experimental results empirically show that the proposed methods ladder mutation-based clonal selection algorithm (LMCSA) and fixed mutation clonal selection algorithm (FMCSA) significantly outperform the existing CLONALG method in terms of quality of the solution, convergence speed, and solution stability

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