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Multi-objective biofilm algorithm (MOBifi) for de novo drug design with special focus to anti-diabetic drugs

APPLIED SOFT COMPUTING 2020年 96卷 106655-106655页

作者： Devi, R. Vasundhara Sathya, S. Siva Coumar, Mohane Selvaraj Pondicherry Univ Dept Comp Sci Pondicherry India Pondicherry Univ Ctr Bioinformat Pondicherry India

swarm intelligence algorithms are inspired by the collective and intelligent behavior of microorganisms, insects, birds or animals, to solve real-world optimization problems. The extraordinary intelligence and coordination observed among bacteria in biofilm formed the basis of a novel Biofilm algorithm introduced earlier. It simulates the life cycle of bacteria in a biofilm in terms of attachment, maturation and dispersal phases. Here, the Biofilm algorithm is extended to solve multi-objective problems. For the multi-objective biofilm algorithm (MOBifi), the concept of pareto dominance is followed. MOBifi is evaluated using CEC 2009 tri-objective benchmark test functions and compared with well-known multi-objective algorithms. Further, the utility of the MOBifi algorithm is substantiated with a real world multi-objective optimization problem, viz., de novo drug design (DNDD) of anti-diabetic drug-like molecules. The newly designed drug-like molecules using MOBifi are evaluated for their novelty and ability to interact with the anti-diabetic drug targets. The results showed that MOBifi could design novel molecules that could be further tested in the laboratory for diabetes treatment. The MOBifi algorithm disclosed here could be extended for other multi-objective optimization problems as well. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Biofilm algorithm Multi-objective optimization De novo drug design Computer-aided drug design Anti-diabetic molecules swarm intelligence algorithm

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A Modified Manta Ray Foraging Optimization for Global Optimization Problems

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IEEE ACCESS 2021年 9卷 128702-128721页

作者： Tang, Andi Zhou, Huan Han, Tong Xie, Lei Air Force Engn Univ Unmanned Aircraft Combat Syst & Technol Air Force Xian 710038 Peoples R China Air Force Engn Univ Xian 710038 Peoples R China

The Manta ray foraging optimization (MRFO) is a novel swarm-based metaheuristic optimizer. It is mainly modeled by simulating three foraging behaviors of the Manta rays, which has a good performance. However, several drawbacks of MRFO have been noticed by analyzing its mathematical model. Random selection of reference points in the early iterations weakens the exploitation capability of MRFO. Chain foraging tends to lead the algorithm into local optimum. In addition, the algorithm suffers from the deficiency of decreasing population diversity in the late iteration. To address these shortcomings, a modified MRFO using three strategies, called m-MRFO, is proposed in this paper. An elite search pool (ESP) is established in this paper to enhance exploitation capability. By using adaptive control parameter strategies (ACP), we expand the range of MRFO's exploration in the early iterations and enhance the accuracy of exploitation in the later iterations, balancing exploiting and exploring capabilities. Furthermore, we use a distribution estimation strategy (DES) to adjust the evolutionary direction using the dominant population information to promote convergence. The m-MRFO performance was verified by selecting 23 classical test functions and CEC2017 test suite. The significance of the results was also verified by Friedman test, Wilcoxon test and Iman-Davenport test. Moreover, we have confirmed the potential of m-MRFO to solve real-world problems by solving three engineering design problems. The simulation results show that the improvement strategy proposed in this paper can effectively improve the performance of MRFO. m-MRFO is highly competitive.

关键词： Statistics Sociology Mathematical model Heuristic algorithms Spirals Cyclones Convergence Manta rays foraging optimizer metaheuristic swarm intelligence algorithm engineering optimization problems

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Improving K-means clustering with enhanced Firefly algorithms

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APPLIED SOFT COMPUTING 2019年 84卷 105763-000页

作者： Xie, Hailun Zhang, Li Lim, Chee Peng Yu, Yonghong Liu, Chengyu Liu, Han Walters, Julie Univ Northumbria Fac Engn & Environm Dept Comp & Informat Sci Computat Intelligence Res Grp Newcastle NE1 8ST England Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Vic 3216 Australia Nanjing Univ Posts & Telecommun Coll Tongda Nanjing Jiangsu Peoples R China Southeast Univ Sch Instrument Sci & Engn State Key Lab Bioelect Nanjing 210096 Jiangsu Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 3AA S Glam Wales

In this research, we propose two variants of the Firefly algorithm (FA), namely inward intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for undertaking the obstinate problems of initialization sensitivity and local optima traps of the K-means clustering model. To enhance the capability of both exploitation and exploration, matrix-based search parameters and dispersing mechanisms are incorporated into the two proposed FA models. We first replace the attractiveness coefficient with a randomized control matrix in the IIEFA model to release the FA from the constraints of biological law, as the exploitation capability in the neighbourhood is elevated from a one-dimensional to multi-dimensional search mechanism with enhanced diversity in search scopes, scales, and directions. Besides that, we employ a dispersing mechanism in the second CIEFA model to dispatch fireflies with high similarities to new positions out of the close neighbourhood to perform global exploration. This dispersing mechanism ensures sufficient variance between fireflies in comparison to increase search efficiency. The ALL-IDB2 database, a skin lesion data set, and a total of 15 UCI data sets are employed to evaluate efficiency of the proposed FA models on clustering tasks. The minimum Redundancy Maximum Relevance (mRMR)-based feature selection method is also adopted to reduce feature dimensionality. The empirical results indicate that the proposed FA models demonstrate statistically significant superiority in both distance and performance measures for clustering tasks in comparison with conventional K-means clustering, five classical search methods, and five advanced FA variants. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Data clustering Firefly algorithm swarm intelligence algorithm K-means clustering

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Adaptive multi-objective swarm fusion for imbalanced data classification

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INFORMATION FUSION 2018年 39卷 1-24页

作者： li, Jinyan Fong, Simon Wong, Raymond K. Chu, Victor W. Univ Macau Dept Comp Informat Sci Macau Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

Learning a classifier from an imbalanced dataset is an important problem in data mining and machine learning. Since there is more information from the majority classes than the minorities in an imbalanced dataset, the classifier would become over-fitted to the former and under-fitted to the latter classes. Previous attempts to address the problem have been focusing on increasing the learning sensitivity to the minorities and/or rebalancing sample sizes among classes before learning. However, how to efficiently identify their optimal mix in rebalancing is still an unresolved problem. Due to non-linear relationships between attributes and class labels, merely to rebalance sample sizes rarely comes up with optimal results. Moreover, brute-force search for the perfect combination is known to be NP-hard and hence a smarter heuristic is required. In this paper, we propose a notion of swarm fusion to address the problem using stochastic swarm heuristics to cooperatively optimize the mixtures. Comparing with conventional rebalancing methods, e.g., linear search, our novel fusion approach is able to find a close to optimal mix with improved accuracy and reliability. Most importantly, it has found to be with higher computational speed than other coupled swarm optimization techniques and iteration methods. In our experiments, we first compared our proposed solution with traditional methods on thirty publicly available imbalanced datasets. Using neural network as base learner, our proposed method is found to outperform other traditional methods by up to 69% in terms of the credibility of the learned classifiers. Secondly, we wrapped our proposed swarm fusion method with decision tree. Notably, it defeated six state-of-the-art methods on ten imbalanced datasets in all evolution metrics that we considered. (C) 2017 Elsevier B.V. All rights reserved.

关键词： swarm fusion swarm intelligence algorithm Multi-objective Crossover rebalancing Imbalanced data classification

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Firefly algorithm With Luciferase Inhibition Mechanism

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IEEE ACCESS 2019年 7卷 120189-120201页

作者： Peng, Hu He, Yichen Deng, Changshou Wu, Zhijian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Wuhan 430072 Hubei Peoples R China

Firefly algorithm (FA) was proposed by Yang which inspired from communication between fireflies through flash. As an efficient swarm intelligence algorithm, FA has been successfully applied in real-world applications. However, FA employs full attraction model that the method of selecting fireflies is sequential selection, namely each firefly can be attracted to other all fireflies in the worst case. This causes FA is prone to fall into local optima and high computational complexity. Inspired by the principle of firefly glow, an enhanced FA variant is proposed, namely, firefly algorithm with luciferase inhibition mechanism (LiFA), where luciferase inhibition mechanism is introduced to improve the effectiveness of selection. Besides, adaptive attraction model is also proposed to reduce the computational complexity and balance exploration and exploitation. Experiments are conducted on CEC2013 test suite to verify the performance of LiFA. The results show that LiFA has the best performance in some complex functions when compared other advanced FA variants.

关键词： Adaptive attraction firefly algorithm luciferase inhibition swarm intelligence algorithm

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Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems

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INTERNATIONAL JOURNAL OF ELECTRONICS 2017年第1期104卷 1-15页

作者： Zhang, Shuying Zhao, Xiaohui Liang, Cong Ding, Xu Jilin Univ Coll Commun Engn Changchun Peoples R China

In cognitive radio (CR) systems, reasonable power allocation can increase transmission rate of CR users or secondary users (SUs) as much as possible and at the same time insure normal communication among primary users (PUs). This study proposes an optimal power allocation scheme for the OFDM-based CR system with one SU influenced by multiple PU interference constraints. This scheme is based on an improved artificial fish swarm (IAFS) algorithm in combination with the advantage of conventional artificial fish swarm (ASF) algorithm and particle swarm optimisation (PSO) algorithm. In performance comparison of IAFS algorithm with other intelligent algorithms by simulations, the superiority of the IAFS algorithm is illustrated;this superiority results in better performance of our proposed scheme than that of the power allocation algorithms proposed by the previous studies in the same scenario. Furthermore, our proposed scheme can obtain higher transmission data rate under the multiple PU interference constraints and the total power constraint of SU than that of the other mentioned works.

关键词： Cognitive radio power allocation OFDM swarm intelligence algorithm AFS algorithm

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Research on efficient-efficient routing protocol for WSNs based on improved artificial bee colony algorithm

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IET WIRELESS SENSOR SYSTEMS 2017年第1期7卷 15-20页

作者： Wang, Huadong Chen, Ying Dong, Shi Zhoukou Normal Univ Sch Comp Sci & Technol Zhoukou 466001 Peoples R China Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

Efficient and reliable routing protocol is one of the key technologies in wireless sensor networks, and it has been concerned by a lot of researchers. In light of the random selection of cluster head in low energy adaptive clustering hierarchy protocol algorithm, the residual energy of nodes, node location and node density are not considered. Through theoretical deduction and mathematical calculation, the study found that it is a NP - hard problem. A swarm intelligence algorithm was introduced into the clustering algorithm of wireless sensor network, and an efficient and reliable clustering algorithm for wireless sensor networks (WSNs) based on quantum artificial bee colony algorithm was proposed. Considering the residual energy of nodes, node location and node density, and the energy consumption of the network is balanced. Experiments show that the proposed routing protocol outperforms other work in most cases by reducing the network energy consumption for WSNs by more than 13%, which indicates the feasibility of the authors' approach for efficient and reliable routing protocol in wireless sensor networks.

关键词： energy conservation routing protocols wireless sensor networks pattern clustering computational complexity optimisation swarm intelligence energy consumption telecommunication power management telecommunication network reliability efficient-efficient routing protocol WSN improved artificial bee colony algorithm wireless sensor network random selection cluster head low energy adaptive clustering hierarchy protocol algorithm NP hard problem swarm intelligence algorithm energy consumption

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Population diversity of particle swarm optimisation algorithms for solving multimodal optimisation problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2018年第1期17卷 69-79页

作者： Cheng, Shi Chen, Junfeng Qin, Quande Shi, Yuhui Univ Nottingham Ningbo China Div Comp Sci Ningbo Zhejiang Peoples R China Hohai Univ Coll IOT Engn Changzhou Peoples R China Shenzhen Univ Dept Management Sci Shenzhen Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou Peoples R China

The aim of multimodal optimisation is to locate multiple peaks/ optima in a single run and to maintain these found optima until the end of a run. In this paper, seven variants of particle swarm optimisation (PSO) algorithms are utilised to solve multimodal optimisation problems. The position diversity is utilised to measure the candidate solutions during the search process. Our goal is to measure the performance and effectiveness of variants of PSO algorithms and investigate why an algorithm performs effectively from the perspective of population diversity. Based on the experimental results, the conclusions could be made that the PSO with ring structure and social-only PSO with ring structure perform better than the other PSO variants on multimodal optimisation. From the population diversity measurement, it is shown that to obtain good performances on multimodal optimisation problems, an algorithm needs to balance its global search ability and solutions maintenance ability.

关键词： swarm intelligence algorithm multimodal optimisation particle swarm optimisation PSO population diversity nonlinear equation systems

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A real-coded chicken swarm optimisation algorithm for solving travelling salesman problem

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2023年第2期17卷 166-181页

作者： Lin, Min Yang, Yuhang Zhong, Yiwen Lin, Juan Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Peoples R China

Chicken swarm optimisation (CSO) algorithm, which is inspired by the hierarchal structure and the behaviours of the chicken flock, was first presented for continuous optimisation problems. The paper proposes a real-coded scheme of CSO algorithm (RCCSO) to solve travelling salesman problem (TSP). In the RCCSO algorithm, each position vector represents a visiting sequence of cities. In a position vector, each dimension represents a city and is coded with a real number. The integer part of the real number represents the index number of the city, and the decimal part denotes the visiting order of the city. Using this coding scheme, the discrete neighbourhood of TSP is converted into a continuous neighbourhood. Two repair operators, relocation operator and replacement operator, are designed to guarantee that position vector is always a valid solution of TSP. Finally, the RCCSO algorithm is compared with many different types of intelligent optimisation algorithms. Experimental results prove that the RCCSO algorithm can find the shortest path more quickly and effectively on most TSP datasets.

关键词： CSO chicken swarm optimisation real-coded scheme TSP travelling salesman problem swarm intelligence algorithm relocation operator replacement operator

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A Levy Flight Sine Cosine algorithm for Global Optimization Problems

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2021年第1期12卷 49-66页

作者： Li, Yu Zhao, Yiran Liu, Jingsen Hena Univ Inst Management Sci & Engn Kaifeng Peoples R China Henan Univ Sch Business Kaifeng Peoples R China Henan Univ Inst Intelligent Network Syst Kaifeng Peoples R China Henan Univ Software Sch Kaifeng Peoples R China

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.

关键词： Engineering Design Problem Function Optimization Global Optimization Problem Levy Flight Simulation Experiment Sine Cosine algorithm Statistical Test swarm intelligence algorithm

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