Multi-population following behavior-driven fruit fly optimization: A Markov chain convergence proof and comprehensive analysis

Wang, Xinyu; Chen, Huiling; Heidari, Ali Asghar; Zhang, Xiang; Xu, Jian; Xu, Yitie; Huang, Hui

doi:10.1016/j.knosys.2020.106437

Cited by 50 publications

(18 citation statements)

References 86 publications

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“…In [75], authors studied the relation of particle distances to determine the state of the particle swarm optimizer. The states were inspired by [38,76]. Parameters are updated according to the determined state.…”

Section: Related Workmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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Section: Related Workmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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“…Swarm-based stochastic methods involve any type of mathematical form and various inspirations. In recent years, metaheuristic algorithms [4] have attracted much attention and have been extensively used in numerous fields [6][7][8][9][10][11][12][13][14][15][16][17] . Such popularity is attributed to the ability of MAs to solve many possible complex feature spaces in practical problems in neural network-based control [18,19] , formation control [20] , deep learning models and feature understanding [21,22] , adaptive control [23,24] , machine learning-based implements [25,26] , and artificial intelligence [27] .…”

Section: Introductionmentioning

confidence: 99%

The Colony Predation Algorithm

et al. 2021

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This paper proposes a new stochastic optimizer called the Colony Predation Algorithm (CPA) based on the corporate predation of animals in nature. CPA utilizes a mathematical mapping following the strategies used by animal hunting groups, such as dispersing prey, encircling prey, supporting the most likely successful hunter, and seeking another target. Moreover, the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals’ selective abandonment behavior. This paper also presents a new way to deal with cross-border situations, whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm’s exploitation ability. The proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm’s efficacy in optimizing engineering problems. The results show that the proposed algorithm exhibits competitive, superior performance in different search landscapes over the other algorithms. Moreover, the source code of the CPA will be publicly available after publication.

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“…More and more Swarm Intelligence (SI) based algorithms have arisen in recent years [1,2] . SI-based algorithms can be a solution to many problems like medical diagnosis [3][4][5][6] , financial distress prediction [7][8][9] , energy field [10][11][12] , engineering problems [13][14][15][16][17][18][19] , feature reduction [20,21] , educational field [22][23][24] , maximum satisfiability problem [25,26] , PID optimization control [27][28][29] , wind speed prediction [30] , fault diagnosis of rolling bearings [31,32] , gate resource allocation [33,34] and scheduling problem [35,36] . SI based algorithms can be classified into two categories: the environment inspires one kind, such as Particle Swarm Optimization (PSO) [37] , Artificial Bee Colony (ABC) [38] , and so on; another kind is inspired by social behavior, for example, Moth-Flame Optimization (MFO) [39] , Harris Hawks Optimizer (HHO) [40] , Slime Mould Algorithm (SMA) [41] , etc.…”

Section: Introductionmentioning

confidence: 99%

Generalized Oppositional Moth Flame Optimization with Crossover Strategy: An Approach for Medical Diagnosis

Xia

Zhang

et al. 2021

J Bionic Eng

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In the original Moth-Flame Optimization (MFO), the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame, so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems. Therefore, in this work, a generalized oppositional MFO with crossover strategy, named GCMFO, is presented to overcome the mentioned defects. In the proposed GCMFO, GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate; crisscross search (CC) is adopted to promote the exploitation and/or exploration ability of MFO. The proposed algorithm’s performance is estimated by organizing a series of experiments; firstly, the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems. Secondly, GCMFO is applied to handle multilevel thresholding image segmentation problems. At last, GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases, including the appendicitis diagnosis, overweight statuses diagnosis, and thyroid cancer diagnosis. Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy. It also indicates that the presented GCMFO has a promising potential for application.

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Multi-population following behavior-driven fruit fly optimization: A Markov chain convergence proof and comprehensive analysis

Cited by 50 publications

References 86 publications

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

The Colony Predation Algorithm

Generalized Oppositional Moth Flame Optimization with Crossover Strategy: An Approach for Medical Diagnosis

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