2006
DOI: 10.1109/tmag.2006.871633
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A modified immune network algorithm for multimodal electromagnetic problems

Abstract: 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 … Show more

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Cited by 44 publications
(12 citation statements)
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“…In this paper, the set of dominant antibodies is denoted as D. These dominant antibodies are the non-dominated individuals in population B. For example, in the antibody population B = {b 1 where ζ(a j , A) denotes the crowding-distance value of the active antibodies a j , and n c is the expected value of the size of the clone population.…”
Section: Non-dominated Clonal Selection and Proportional Cloningmentioning
confidence: 99%
See 1 more Smart Citation
“…In this paper, the set of dominant antibodies is denoted as D. These dominant antibodies are the non-dominated individuals in population B. For example, in the antibody population B = {b 1 where ζ(a j , A) denotes the crowding-distance value of the active antibodies a j , and n c is the expected value of the size of the clone population.…”
Section: Non-dominated Clonal Selection and Proportional Cloningmentioning
confidence: 99%
“…This function gives AIS the unique characteristic of guaranteeing the survival of the variant offspring that better match the antigen. In the literature, several authors (Chung et (Whitbrook et al 2007), optimization (Miyamoto et al 2004; Campelo et al 2006;Xu et al 2014), parameter estimation (Liu et al 2009), and other similar machine learning problem domains (Harmer et al 2002;Dasgupta et al 2004). In recent years, AIS has been applied to solving MOPs, and studies show remarkable performances.…”
Section: Introductionmentioning
confidence: 99%
“…Apart from the DE algorithm, various methodologies have been introduced in the EC literature which aim to optimize multimodal problems. Representative examples include the topological species conservation approach [14], Particle Swarm Optimizers [25]- [27], evolutionary strategies [28], multi-objective algorithms [29], and artificial immune systems [30], [31].…”
Section: Related Workmentioning
confidence: 99%
“…A review of common algorithms within the field can be found in [5,10]. AIS algorithms are diverse and have contributed to the fields of optimisation [2], classification [22] and anomaly detection [20]. The dendritic cell algorithm (DCA) is an emerging AIS algorithm based on biological dendritic cells (DCs).…”
Section: Introductionmentioning
confidence: 99%