An immune multi-objective optimization algorithm with differential evolution inspired recombination

Qi, Yutao; Hou, Zhanting; Yin, Minglei; Sun, Hui; Huang, Jianbin

doi:10.1016/j.asoc.2015.01.012

Cited by 48 publications

(12 citation statements)

References 37 publications

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“…By rephrasing stability selection as a multi-objective optimization problem, we can jointly run over various model complexities and find the corresponding optimal structures for each model complexity. In this paper, we have used NSGA-II for multi-objective optimization, because of its popularity and availability, but realize that more recent multiobjective optimization approaches [54], [55], [56], [57] may be even more efficient. This is beyond the scope of this work and left for future research.…”

Section: Discussionmentioning

confidence: 99%

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling

Rahmadi

Groot

Heins

et al. 2017

Applied Soft Computing

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Causal modeling has long been an attractive topic for many researchers and in recent decades there has seen a surge in theoretical development and discovery algorithms. Generally discovery algorithms can be divided into two approaches: constraint-based and score-based. The constraint-based approach is able to detect common causes of the observed variables but the use of independence tests makes it less reliable. The score-based approach produces a result that is easier to interpret as it also measures the reliability of the inferred causal relationships, but it is unable to detect common confounders of the observed variables. A drawback of both score-based and constrained-based approaches is the inherent instability in structure estimation. With finite samples small changes in the data can lead to completely different optimal structures. The present work introduces a new hypothesis-free score-based causal discovery algorithm, called stable specification search, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Structure search is performed over Structural Equation Models. Our approach uses exploratory search but allows incorporation of prior background knowledge. We validated our approach on one simulated data set, which we compare to the known ground truth, and two real-world data sets for Chronic Fatigue Syndrome and Attention Deficit Hyperactivity Disorder, which we compare to earlier medical studies. The results on the simulated data set show significant improvement over alternative approaches and the results on the real-word data sets show consistency with the hypothesis driven models constructed by medical experts.

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Section: Discussionmentioning

confidence: 99%

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling

Rahmadi

Groot

Heins

et al. 2017

Applied Soft Computing

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“…Multi-objective optimisation refers to the process of simultaneously optimising two or more conflicting objectives subject to some given constraints. Multi-objective immune algorithm simulate the antigen-antibody reaction of the immune system in mammals (Qi et al, 2016(Qi et al, , 2015. In particular, the antigen and the antibody are equivalent to the objective function and the feasible solution for an optimisation problem (Lin et al, 2015;Liang et al, 2015).…”

Section: The Proposed Node Deployment Methods Based On Multi-objectivementioning

confidence: 99%

Wireless sensor network node deployment based on multi-objective immune algorithm

2018

IJIPT

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Wireless sensor network (WSN) is made up of a large number of low-cost wireless sensor nodes, which can collect all types of data in its lifetime. Node deployment of WSN is a NP complete problem, and it can significantly influence the network coverage and energy consumption in WSN. In this paper, we try to exploit multi-objective immune algorithm to solve the node deployment problem in WSN. The proposed node deployment algorithm aims to maximise the degree of network coverage and minimise the energy consumption. In the proposed multi-objective immune algorithm, each antibody refers to a candidate solution in node deployment process, and the antibodies are randomly initialised with a specific range. In addition, we design a fitness function by two ranking modes. Finally, experiments are conducted to test the performance of the proposed algorithm. Particularly, we use some typical performance metric in this experiment, such as coverage degree, energy consumption, and maximum moved distance. Experimental results demonstrate that the proposed can both enhance the network coverage degree and reduce the energy consumption by minimising the moved distance.

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“…In NNIA, the selection of active antibodies is based on the crowding distance [28]. However, there are shortcomings of this method.…”

Section: B Grid-based Selection Of Active Antibodiesmentioning

confidence: 99%

“…Differential evolution (DE) [28] is a powerful stochastic search method, which has been widely used in multi-objective evolutionary algorithms. In this paper, we propose a hybrid differential evolution strategy consisting of two well-known DE strategies [36], i.e., rand/1/bin and best/1/bin, denoted by DE1 and DE2 respectively.…”

Section: Hybrid Differential Evolution Strategymentioning

confidence: 99%

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using Improved Multi-Objective Immune Algorithm

et al. 2019

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Clustering algorithms have attracted a lot of attentions recently in real-world applications. However, the traditional clustering algorithms still have plenty of defects which are not yet resolved. In this paper, a kernel-based intuitionistic fuzzy C-means clustering using improved multi-objective artificial immune algorithm (KIFCM-IMOIA) is proposed. In our algorithm, the kernel trick and the intuitionistic fuzzy entropy (IFE) are introduced into the objective functions, which improves the robustness to noises. In addition, an improved multi-objective optimization immune algorithm (IMOIA), which simultaneously optimizes the intra-cluster compactness and inter-cluster separation, is proposed to prevent the algorithm from falling into local optimum. The proposed IMOIA uses a novel active antibody selection strategy, a hybrid differential evolution strategy, and an adaptive mutation operator to maintain better distribution of the solutions with better convergence. Finally, we performed experiments using 14 UCI datasets and compared our algorithm with six clustering methods on three performance metrics. The experimental results show that our algorithm performs better than other algorithms. INDEX TERMS Intuitionistic fuzzy C-means, kernel function, artificial immune algorithm, multi-objective optimization.

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An immune multi-objective optimization algorithm with differential evolution inspired recombination

Cited by 48 publications

References 37 publications

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling

Wireless sensor network node deployment based on multi-objective immune algorithm

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using Improved Multi-Objective Immune Algorithm

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