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A cluster-based immune-inspired algorithm using manifold learning for multimodal multi-objective optimization

INFORMATION SCIENCES 2021年 581卷 304-326页

作者： Zhang, Weiwei Zhang, Ningjun Zhang, Weizheng Yen, Gary G. Li, Guoqing Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou 450000 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Both problem characteristics in multimodality and multi-objective are involved in multi modal multi-objective optimization problems (MMOPs). How to locate diverse Pareto sets and approximate Pareto front simultaneously is a challenging research topic. To address this issue, a cluster-based immune-inspired algorithm using manifold learning is proposed in this paper for solving MMOPs. First of all, the population is partitioned into multiple sub populations, and each of them is expected to find equivalent Pareto solutions in different regions. Subsequently, the immune-inspired algorithm with proportional cloning and hypermutation is developed for improving the diversity of the population and obtaining high-quality Pareto solutions in the decision space. Additionally, principal component analysis is adopted to learn the manifold of the Pareto set, further improve the convergence, and enhance interaction among subpopulations. The proposed algorithm is compared with six state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm is capable of locating equivalent Pareto optimal solutions in the decision space and maintaining the diversity and convergence of solutions in both decision space and objective space, simultaneously. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Multimodal multi-objective optimization immune-inspired algorithm Manifold learning Principal component analysis

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cobICA: A Concentration-Based, immune-inspired algorithm for ICA Over Galois Fields

cobICA: A Concentration-Based, Immune-Inspired Algorithm for...

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IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)

作者： Silva, Daniel G. Montalvao, Jugurta Attux, Romis Univ Brasilia Dept Elect Engn Brasilia DF Brazil Univ Estadual Campinas Campinas SP Brazil Univ Fed Sergipe Sao Cristovao SE Brazil Univ Estadual Campinas DSPCom Lab Campinas SP Brazil

ISBN: (纸本)9781479945030

An interesting and recent application of population-based metaheuristics resides in an unsupervised signal processing task: independent component analysis (ICA) over finite fields. Based on a state-of-the-art immune-inspired method, this work proposes a new ICA algorithm for finite fields of arbitrary order that employs mutation and local search operators specifically customized to the problem domain. The results obtained with the new technique indicate that the proposal is effective in performing component separation, and the analysis includes a preliminary study on image separation.

关键词： Galois fields independent component analysis mathematical operators search problems signal processing arbitrary order cobICA component separation concentration-based algorithm finite fields image separation immune-inspired algorithm immune-inspired method local search operator mutation operator population-based metaheuristics unsupervised signal processing task algorithm design and analysis Cloning Entropy immune system Search problems Sociology Statistics

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Density Preservation and Vector Quantization in immune-inspired algorithms

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9th International Conference on Artificial immune Systems (ICARIS 2010)

作者： Azzolini, Alisson G. Violato, Ricardo P. V. Von Zuben, Fernando J. Univ Estadual Campinas Sch Elect & Comp Engn BR-13083970 Campinas SP Brazil

ISBN: (纸本)9783642145469

A clustering algorithm may be designed to generate prototypes capable of minimizing the cumulative distance between each sample in the dataset and its corresponding prototype, denoted as minimum quantization error clustering. On the other hand, some clustering applications may require density-preserving prototypes, more specifically prototypes that maximally obey the original density distribution of the dataset. This paper presents a conceptual framework to demonstrate that both criteria are attainable but are distinct and cannot be fulfilled simultaneously. Illustrative examples are used to validate the framework, further applied to produce an adaptive radius immune-inspired algorithm capable of transiting between both criteria in practical applications.

关键词： immune-inspired algorithm Data clustering Vector quantization Density preservation

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An immune-inspired approach to qualitative system identification of biological pathways

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NATURAL COMPUTING 2011年第1期10卷 189-207页

作者： Pang, Wei Coghill, George M. Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

In this paper, a special-purpose qualitative model learning (QML) system using an immune-inspired algorithm is proposed to qualitatively reconstruct biological pathways. We choose a real-world application, the detoxification pathway of Methylglyoxal (MG), as a case study. First a converter is implemented to convert possible pathways to qualitative models. Then a general learning strategy is presented. To improve the scalability of the proposed QML system and make it adapt to future more complicated pathways, a modified clonal selection algorithm (CLONALG) is employed as the search strategy. The performance of this immune-inspired approach is compared with those of exhaustive search and two backtracking algorithms. The experimental results indicate that this immune-inspired approach can significantly improve the search efficiency when dealing with some complicated pathways with large-scale search spaces.

关键词： Clonal selection algorithm immune-inspired algorithm Pathway reconstruction Qualitative differential equation Qualitative model learning Qualitative reasoning Qualitative simulation

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An immune-inspired approach to qualitative system identification of biological pathways

An immune-inspired approach to qualitative system identifica...

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15th International Conference on DNA Computing and Molecular Programming

作者： Pang, Wei Coghill, George M. Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

关键词： Clonal selection algorithm immune-inspired algorithm Pathway reconstruction Qualitative differential equation Qualitative model learning Qualitative reasoning Qualitative simulation

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Antibodies with Adaptive Radius as Prototypes of High-Dimensional Datasets

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9th International Conference on Artificial immune Systems (ICARIS 2010)

作者： Violato, Ricardo P. V. Azzolini, Alisson G. Von Zuben, Fernando J. Univ Estadual Campinas Sch Elect & Comp Engn BR-13083970 Campinas SP Brazil

ISBN: (纸本)9783642145469

An adaptive radius immune algorithm proposed in the literature, denoted as ARIA, is claimed to preserve the density distribution of the original dataset when generating prototypes. Density-preserving prototypes may correspond to high-quality compact representations for clustering applications. The original samples in the dataset are interpreted as antigens, and the prototypes are interpreted as antibodies. In this paper, some theoretical results are provided to demonstrate that the original version of ARIA is not capable of generating density-preserving prototypes when high-dimensional datasets are considered. Further, the same theoretical results are explored to conceive a new version of ARIA, now capable of exhibiting the announced density-preserving attribute. The main innovation is in the way the algorithm estimates local densities.

关键词： immune-inspired algorithm adaptive radius prototypes density estimation

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