Immune-based algorithms for dynamic optimization

Trojanowski, Krzysztof; Wierzchoń, Sławomir T.

doi:10.1016/j.ins.2008.11.014

Cited by 69 publications

(50 citation statements)

References 61 publications

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“…Later work applied immune system paradigms to the field of computer security 30,31 , which seemed to act as a catalyst for further investigation of the immune system as a metaphor in many areas, such as anomaly detection 23,32 , pattern recognition 33,34 , sensor fusion and configuration 35 , rule extraction 36 , and optimization 5,7,[37][38][39] . As far as multiobjective optimization is concerned, MISA 40 is the first attempt to solve general multiobjective optimization problems using artificial immune systems.…”

Section: Artificial Immune System and Inspired Optimization Algorithmsmentioning

confidence: 99%

A New Clonal Selection Immune Algorithm with Perturbation Guiding Search and Nonuniform Hypermutation

Zhao¹,

Zhao²,

Niu³

et al. 2010

IJCIS

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A new clonal selection immune algorithm with perturbation guiding search and non-uniform hypermutation (nCSIA) is proposed based on the idea of perturbed particle swarm algorithm and non-uniform mutation. The proposed algorithm proportional clones antibody based on the affinity, adaptively adjusts the searching steps of antibodies with hypermutation according to the adaptive variation rule of non-uniform mutation and chooses the promising antibody based on the affinity by clonal selection principle. In order to keep the balance of exploration/exploitation better, perturbation guiding search strategy is presented, which is actually an elitist learning mechanism and is borrowed from the perturbed particle swarm algorithm. In order to validate the effectiveness of nCSIA, comprehensive experiments and analysis are done based on fifteen unimodal or multimodal benchmark functions. Compared with standard and the recent algorithms, it indicates that the proposed algorithm is feasible, effective and has better performance in terms of convergence, accuracy and stability. More evident predominance emerges from further experimental comparisons with expanding search space and increasing dimensions.

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Section: Artificial Immune System and Inspired Optimization Algorithmsmentioning

confidence: 99%

A New Clonal Selection Immune Algorithm with Perturbation Guiding Search and Nonuniform Hypermutation

Zhao¹,

Zhao²,

Niu³

et al. 2010

IJCIS

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“…Additionally, although there is a wealth of work within the optimisation field addressing dynamic optimisation in which the fitness function applied to a single problem instance changes over time (e.g. [19]), we are not aware of other work that tackles problems in which the fitness function remains static but the characteristics of the instances presented varies over time.…”

Section: Introductionmentioning

confidence: 99%

On the Life-Long Learning Capabilities of a NELLI*: A Hyper-Heuristic Optimisation System

Hart

Sim

2014

Parallel Problem Solving From Nature – PPSN XIII

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Abstract. Real-world applications of optimisation techniques place more importance on finding approaches that result in acceptable quality solutions in a short time-frame and can provide robust solutions, capable of being modified in response to changes in the environment than seeking elusive global optima. We demonstrate that a hyper-heuristic approach NELLI* that takes inspiration from artifical immune systems is capable of life-long learning in an environment where problems are presented in a continuous stream and change over time. Experiments using 1370 bin-packing problems show excellent performance on unseen problems and that the system maintains memory, enabling it to exploit previously learnt heuristics to solve new problems with similar characteristics to ones solved in the past.

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“…Consequently, they can not be adapted to the new environment when change occurs and they progressively loose diversity through generations. To address this problem of diversity 4 , several approaches based on GAs have been developed such as increasing mutation rate 10 , using the immigrants approaches 17,9,28,22 , using explicit or implicit memory 29,5,24 , spreading out the population by multiple sub-populations approaches 6,27 , promoting diversity by an artificial objective function 31 , and improving GAs by hybridization strategies 8,32 . To maintain diversity in population, GAs parameters must be taken into consideration and adapted dynamically.…”

Section: Introductionmentioning

confidence: 99%

Parameters control in GAs for dynamic optimization

Jebari¹,

Bouroumi²,

Ettouhami³

2013

IJCIS

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The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters. Yet the control of parameters has received much attention in the case of static optimization problems, its investigation in the case of dynamic optimization problems (DOPs) is certainly a promising area of search. Indeed, in the case of DOPs the problem is not just to find the optima but to track the moving optima over time, so the parameters must be adapted to this dynamic environment. The proposed algorithm Parameters Control for Dynamic Optimization (PCDO) is based on Genetic Algorithm with Fitness Sharing (GAFS). To solve DOPs by controlling GAs parameters, PCDO uses several strategies. First, an unsupervised fuzzy clustering method is used to track multiple optimums and to perform GAFS. Second, a modified enthusiasm selection is used to adjust the selection pressure. Third, a clustering multi non uniform Mutation is utilized to locate an unexplored search space. Fourth, a novel technique with multiple crossover is applied to guide the algorithm in promising regions of the search space. Fifth, a self adaptive mutation rate is evolved through generation with a learned parameter, in order to control the diversity of the population. In the concern of maintaining the diversity of the population, a new genetic operator called Fertilization is proposed. PCDO is tested on six problems generated from Generalized Dynamic Benchmark Generator (GDBG). Experimental results demonstrate that PCDO outperforms other GAs on DOPs. Moreover, the ability of PCDO to maintain diversity is demonstrated by a new diversity measure.

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Immune-based algorithms for dynamic optimization

Cited by 69 publications

References 61 publications

A New Clonal Selection Immune Algorithm with Perturbation Guiding Search and Nonuniform Hypermutation

A New Clonal Selection Immune Algorithm with Perturbation Guiding Search and Nonuniform Hypermutation

On the Life-Long Learning Capabilities of a NELLI*: A Hyper-Heuristic Optimisation System

Parameters control in GAs for dynamic optimization

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