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Multi-species Cuckoo Search algorithm for Global Optimization

COGNITIVE COMPUTATION 2018年第6期10卷 1085-1095页

作者： Yang, Xin-She Deb, Suash Mishra, Sudhanshu K. Middlesex Univ Sch Sci & Technol Dept Design Engn & Math London NW4 4BT England Victoria Univ Decis Sci & Modelling Program Melbourne Vic Australia North Eastern Hill Univ Dept Econ Shillong Meghalaya India

Many optimization problems in science and engineering are highly nonlinear and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms. The standard cuckoo search (CS) is an optimization algorithm based on a single cuckoo species and a single host species. This work extends the standard CS by using the successful features of the cuckoo-host co-evolution with multiple interacting species. The proposed multi-species cuckoo search (MSCS) intends to mimic the co-evolution among multiple cuckoo species that compete for the survival of the fittest. The solution vectors are encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal case studies in practical applications. Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and all optimal solutions are achievable in the tested cases. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm. The comparison has demonstrated the efficiency of the present algorithm. Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases. Therefore, the proposed approach can be a very effective tool for solving nonlinear global optimization problems.

关键词： Cuckoo search nature-inspired algorithm Multi-species cuckoo search Optimization Swarm intelligence

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Tracking control of redundant mobile manipulator: An RNN based metaheuristic approach

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NEUROCOMPUTING 2020年 400卷 272-284页

作者： Khan, Ameer Hamza Li, Shuai Chen, Dechao Liao, Liefa Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Swansea Univ Dept Elect & Elect Engn Swansea SA1 8EN W Glam Wales Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310008 Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China

In this paper, we propose a topology of Recurrent Neural Network (RNN) based on a metaheuristic optimization algorithm for the tracking control of mobile-manipulator while enforcing nonholonomic constraints. Traditional approaches for tracking control of mobile robots usually require the computation of Jacobian-inverse or linearization of its mathematical model. The proposed algorithm uses a nature-inspired optimization approach to directly solve the nonlinear optimization problem without any further transformation. First, we formulate the tracking control as a constrained optimization problem. The optimization problem is formulated on position-level to avoid the computationally expensive Jacobian-inversion. The nonholonomic limitation is ensured by adding equality constraints to the formulated optimization problem. We then present the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm to solve the optimization problem efficiently using very few mathematical operations. We present a theoretical analysis of the proposed algorithm and show that its computational cost is linear with respect to the degree of freedoms (DOFs), i.e., O(m). Additionally, we also prove its stability and convergence. Extensive simulation results are prepared using a simulated model of IIWA14, a 7-DOF industrial-manipulator, mounted on a differentially driven cart. Comparison results with particle swarm optimization (PSO) algorithm are also presented to prove the accuracy and numerical efficiency of the proposed controller. The results demonstrate that the proposed algorithm is several times (around 75 in the worst case) faster in execution as compared to PSO, and suitable for real-time implementation. The tracking results for three different trajectories;circular, rectangular, and rhodonea paths are presented. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mobile-manipulator Tracking control Metaheuristic optimization Recurrent neural network nature-inspired algorithm Redundancy resolution

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Investigation of cricket behaviours as evolutionary computation for system design optimization problems

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MEASUREMENT 2015年 68卷 225-235页

作者： Canayaz, Murat Karci, Ali Comp Sci Res & Applicat Ctr TR-65080 Van Turkey Fac Engn Dept Comp Engn TR-44280 Malatya Turkey

In this study, the behaviours of an insect species called cricket were investigated and tried to develop a new meta-heuristic algorithm approach that may be used in solving optimization problems by modelling these behaviours. These insect species make a sound by flapping their wings and attract the other crickets around them. While creating this algorithm, the physics laws related to propagation of sound as well as the crickets ability to predict the temperature with the number of flaps were also considered. The approach performance was tried to be shown by applying the developed approach at the end of the study to both numeric problems and cantilever stepped and welded beam that are of system design optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： nature-inspired algorithm Meta-heuristic algorithm Optimization Cricket Design problems

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A comprehensive review of moth-flame optimisation: variants, hybrids, and applications

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2020年第4期32卷 705-725页

作者： Hussien, Abdelazim G. Amin, Mohamed Abd El Aziz, Mohamed Fayoum Univ Fac Sci Al Fayyum Egypt Menoufia Univ Fac Sci Shibn Al Kawm Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt

Moth-flame Optimisation algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a huge interest due to its impressive characteristics mainly: no derivation information needed in the starting phase, few numbers of parameters, simple in implementation, scalable and flexible. Till now, different variants to solve various optimisation problems such as binary, real(continuous), constraint, single-objective, multi-objective, and multimodal MFO has been introduced. Many research papers have been presented and summarised. In this review, a general overview of MFO is presented at first. Then, different variants of MFO are described which are classified into three classes: modified, hybridised, and multi-objective. Furthermore, applications of MFO in Engineering, Computer Science, Wireless Sensor Networks, and other fields are discussed. Finally, many possible and future directions are provided.

关键词： Moth-flame optimisation swarm intelligence meta-heuristics optimisation nature-inspired algorithm

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Impacts of Linnik Flight Usage Patterns on Cuckoo Search for Real-Parameter Global Optimization Problems

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IEEE ACCESS 2019年 7卷 83932-83961页

作者： Nasa-Ngium, Patchara Sunat, Khamron Chiewchanwattana, Sirapat Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen 40002 Thailand

Several contemporary algorithms, including cuckoo search (CS), were applied to the CEC 2017 problem set, which includes a wide variety of 120 very difficult subproblems. We found that the algorithms were ineffective, especially when the number of dimensions was high. We configured several usage patterns of Linnik flight with the inverse of the golden ratio (1/phi) to replace Levy flight in CS, resulting in a new search mechanism that increased the efficiency of the CS algorithm. The impacts of each Linnik flight usage pattern were evaluated using the CEC 2017. The experimental results showed that: 1) CS variants that used Linnik flight were more capable than CS variants that used Levy flight and 2) CS variants that used a mixture of Linnik flight and quantum-behaved mechanisms were even more capable. The primary effect of Linnik flight is the strengthening of the ranking, while that of the quantum-behaved mechanisms is a decreased error. A chaotic-initialized quantum-Linnik flight CS (CQLCS) algorithm is proposed. Among the 66 competitive methods applied to the CEC 2017, CQLCS ranked first and won the contemporary competitive algorithms section, which also included several advanced variants of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The CQLCS could potentially be improved further by adjusting the probability of the occurrence of Linnik flight. The processes for building improved variants were analyzed to discover how, or if, this improvement could be achieved. Finally, the CQLCS algorithm required fewer lines of code to run than did the DE and PSO variants.

关键词： Cuckoo search Levy fiight Linnik fiight heavy-tailed distribution nature-inspired algorithm optimization problems

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A mayfly optimization algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 145卷 106559-106559页

作者： Zervoudakis, Konstantinos Tsafarakis, Stelios Tech Univ Crete Sch Prod Engn & Management Khania Greece

This paper introduces a new method called the Mayfly algorithm (MA) to solve optimization problems. inspired from the flight behavior and the mating process of mayflies, the proposed algorithm combines major advantages of swarm intelligence and evolutionary algorithms. To evaluate the performance of the proposed algorithm, 38 mathematical benchmark functions, including 13 CEC2017 test functions, are employed and the results are compared to those of seven state of the art well-known metaheuristic optimization methods. The MA's performance is also assessed through convergence behavior in multi-objective optimization as well as using a real-world discrete flow-shop scheduling problem. The comparison results demonstrate the superiority of the proposed method in terms of convergence rate and convergence speed. The processes of nuptial dance and random flight enhance the balance between algorithm's exploration and exploitation properties and assist its escape from local optima.

关键词： Mayfly algorithm nature-inspired algorithm Optimization

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A comprehensive review of krill herd algorithm: variants, hybrids and applications

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第1期51卷 119-148页

作者： Wang, Gai-Ge Gandomi, Amir H. Alavi, Amir H. Gong, Dunwei China Univ Petr Huadong Coll Comp Qingdao Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao Peoples R China Jiangsu Normal Univ Sch Comp Sci Xuzhou Jiangsu Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun Jilin Peoples R China Northeast Normal Univ Sch Comp Sci Changchun Jilin Peoples R China Stevens Inst Technol Sch Business Hoboken NJ 07030 USA Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao Peoples R China

Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

关键词： Krill herd Engineering optimization Swarm intelligence Metaheuristic nature-inspired algorithm

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A Physarum-inspired algorithm for Minimum-Cost Relay Node Placement in Wireless Sensor Networks

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IEEE-ACM TRANSACTIONS ON NETWORKING 2020年第2期28卷 681-694页

作者： Sun, Yahui Rehfeldt, Daniel Brazil, Marcus Thomas, Doreen Halgamuge, Saman Australian Natl Univ Res Sch Engn Canberra ACT 2601 Australia Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Tech Univ Berlin Dept Math D-10623 Berlin Germany Univ Melbourne Sch Elect Mech & Infrastruct Engn Melbourne Vic 3010 Australia Australian Natl Univ Canberra ACT 2601 Australia

Relay node placement, which aims to connect pre-deployed sensor nodes to base stations, is essential in minimizing the costs of wireless sensor networks. In this paper, we formulate the new Node-Weighted Partial Terminal Steiner Tree Problem (NWPTSTP) for minimum-cost relay node placement in two-tiered wireless sensor networks. The objective is to minimize the sum of heterogeneous production and placement costs of relay nodes and the sum of outage probabilities of transmission routes in a routing tree simultaneously. This extends the previous work that considers the costs of relay nodes to be homogeneous. After formulating NWPTSTP for this purpose, we prove that it can be transformed to the existing node-weighted Steiner tree problem. Subsequently, we conduct some theoretical analyses on the emerging Physarum-inspired algorithms to reveal their potential of computing Steiner trees. Based on these analyses, we propose a new Physarum-inspired algorithm for solving NWPTSTP. We conduct computational trials to show that: 1) in comparison to a state-of-the-art approximation algorithm for solving the node-weighted Steiner tree problem, our Physarum-inspired algorithm can produce better solutions in a smaller amount of time;and 2) in comparison to two state-of-the-art relay node placement algorithms, our Physarum-inspired algorithm can design wireless sensor networks with 25% lower relay cost and similar quality of service (specifically, 5% shorter network lifetime, 2% longer delay, and 0% loss of goodput). This indicates the usefulness of our Physarum-inspired algorithm for minimum-cost relay node placement in budget-limited scenarios.

关键词： Relays Steiner trees Wireless sensor networks Routing Power system reliability Probability Approximation algorithms Physarum polycephalum Steiner tree problem nature-inspired algorithm Internet of Things

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Modeling information diffusion in online social networks using a modified forest-fire model

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2021年第2期56卷 355-377页

作者： Kumar, Sanjay Saini, Muskan Goel, Muskan Panda, B. S. Delhi Technol Univ Dept Comp Sci & Engn Main Bawana Rd New Delhi 110042 India Indian Inst Technol Delhi Dept Math Comp Sci & Applicat Grp Hauz Khas New Delhi 110016 India Microsoft India Hyderabad 500032 Telangana India Microsoft India Bangalore 560025 Karnataka India

Information dissemination has changed rapidly in recent years with the emergence of social media which provides online platforms for people worldwide to share their thoughts, activities, emotions, and build social relationships. Hence, modeling information diffusion has become an important area of research in the field of network analysis. It involves the mathematical modeling of the movement of information and study the information spread pattern. In this paper, we attempt to model information propagation in online social networks using a nature-inspired approach based on a modified forest-fire model. A slight spark can start a wildfire in a forest, and the spread of this fire depends on vegetation, weather, and topography, which may act as fuel. On similar lines, we labeled users who haven't joined the network yet asEmpty, existing users asTree, and information asFire. The spread of information across online social networks depends upon users-followers relationships, the significance of the topic, and other such features. We introduce a novelBurntstate to the traditional forest-fire model to represent non-spreaders in the network. We validate our method on six real-world data-sets extracted from Twitter and conclude that the proposed model performs reasonably well in predicting information diffusion.

关键词： Information diffusion Forest-fire model nature-inspired algorithm Online social networks Twitter

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Chemical Reaction Optimization: a tutorial

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MEMETIC COMPUTING 2012年第1期4卷 3-17页

作者： Lam, Albert Y. S. Li, Victor O. K. Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

Chemical Reaction Optimization (CRO) is a recently established metaheuristics for optimization, inspired by the nature of chemical reactions. A chemical reaction is a natural process of transforming the unstable substances to the stable ones. In microscopic view, a chemical reaction starts with some unstable molecules with excessive energy. The molecules interact with each other through a sequence of elementary reactions. At the end, they are converted to those with minimum energy to support their existence. This property is embedded in CRO to solve optimization problems. CRO can be applied to tackle problems in both the discrete and continuous domains. We have successfully exploited CRO to solve a broad range of engineering problems, including the quadratic assignment problem, neural network training, multimodal continuous problems, etc. The simulation results demonstrate that CRO has superior performance when compared with other existing optimization algorithms. This tutorial aims to assist the readers in implementing CRO to solve their problems. It also serves as a technical overview of the current development of CRO and provides potential future research directions.

关键词： Chemical Reaction Optimization Metaheuristic nature-inspired algorithm Approximate algorithm Optimization

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