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A survey on evolutionary algorithms dynamics and its complexity - Mutual relations, past, present and future

swarm AND EVOLUTIONARY COMPUTATION 2015年 25卷 2-14页

作者： Zelinka, Ivan Ton Duc Thang Univ Div MERLIN Ho Chi Minh City Vietnam VSB Tech Univ Ostrava Dept Comp Sci Fac Elect Engn & Comp Sci Ostrava Czech Republic

swarm and evolutionary based algorithms represent a class of search methods that can be used for solving optimization problems. They mimic natural principles of evolution and swarm based societies like ants, bees, by employing a population-based approach in mutual communication and information sharing and processing, including randomness. In this paper, history of swarm and evolutionary algorithms are discussed in general as well as their dynamics, structure and behavior. The core of this paper is an overview of an alternative way how dynamics of arbitrary swarm and evolutionary "algorithms can be visualized, analyzed and controlled. Also selected representative applications are discussed at the end. Both subtopics are based on interdisciplinary intersection of two interesting research areas: swarm and evolutionary algorithms and complex dynamics of nonlinear systems that usually exhibit very complex behavior. (C) 2015 Elsevier B.V. All rights reserved.

关键词： swarm algorithms Complex systems Deterministic chaos Control Synthesis Evolutionary algorithms

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Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 64卷 401-414页

作者： Sankar, K. Prajindra Kiong, Tiong Sieh Paw, Johnny Koh Siaw Univ Tenaga Nas Coll Engn Elect & Commun Engn Dept Kajang Selangor Malaysia Univ Tenaga Nas Inst Sustainable Energy Kajang Selangor Malaysia

The ever evolving complexity of real-world problems had become an impetus for the development of many new and efficient optimization algorithms. Meta-heuristics based on evolutionary computation and swarm intelligence are successful examples of nature-inspired optimization techniques. in this work, a new Dynamic Social Behavior (DSB) algorithm is proposed to solve global optimization problems. The DSB algorithm is based on the simulation of cooperative behavior of animal groups. In the proposed algorithm, individuals emulate the interaction of individuals based on biological laws of cooperative colony. This algorithm partially adopts the foraging strategy of animal groups and utilizes recruitment signal as a means of information transfer among individuals. In order to illustrate the proficiency and robustness of the proposed algorithm, it is compared with other well-known evolutionary algorithms. The comparison examines several series of widely used benchmark functions and an engineering problem on hyper beamforming optimization. The results testifies the superior performance of DSB compared with other state-of-the-art meta-heuristics. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Meta-heuristics Optimization swarm algorithms Global optimization Bio-inspired algorithms Beamforming

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VSC-HVDC system optimized PI controllers using bacterial foraging algorithm

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ELECTRICAL ENGINEERING 2015年第3期97卷 205-212页

作者： Mazouz, Lakhdar Zidi, Sid Ahmed Saadi, Slami Khatir, Mohamed Benmessaoud, Tahar Sidi Belabbes Univ ICEPS Lab Sidi Bel Abbes Algeria Djelfa Univ Fac Sci & Technol Djelfa Algeria

The strategy of control for high-voltage direct current system on voltage-source converter VSC-HVDC is based on an important element which is PI controller. Because of its simple structure and strong robustness in a wide range of serving conditions, it has been used in the control system in the last few years. Therefore, it is important for the VSC-HVDC system permanent regime to choose proper PI parameters. The use of conventional techniques to find suitable PI parameters creates a number of challenges for the system operators, because it is a difficult process and time consuming. In this paper, a new swarm intelligence algorithm called bacterial foraging optimization is introduced to find the optimal parameters of the PI controller. This approach offers great flexibility for the permanent regime recovery and improves the stability of the VSC-HVDC link compared to conventional techniques. The obtained results are presented to show the effectiveness of the proposed swarm implementation for optimal controller design for VSC-HVDC transmission. MATLAB/Similink simulations are provided to demonstrate the performance of the proposed approach.

关键词： VSC-HVDC BFO approach Optimization PI controller swarm algorithms Parameters Performance

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Simultaneous feature selection and SVM optimization based on fuzzy signature and chaos GOA

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EVOLVING SYSTEMS 2024年第5期15卷 1907-1937页

作者： Zandvakili, Aboozar Javidi, Mohammad Masoud Mansouri, Najme Shahid Bahonar Univ Kerman Dept Comp Sci Kerman Iran Bahonar Univ Kerman Fac Shahid Kerman Iran

In machine learning models, feature selection plays a crucial role. It reduces overall data, minimizes storage requirements, and enhances algorithm performance. Despite this, greedy and exhaustive search methods may not be optimal as the number of features increases. Metaheuristic algorithms are a more sensible way to deal with this issue. In this study, the Grasshopper Optimization Algorithm (GOA) is applied to feature selection. Although GOA may be relatively easy to implement, it may not fully leverage each iteration and may become stuck in local optima. The comfort zone in GOA influences the grasshopper movement within the search space, influencing exploration and exploitation. As a constant, the algorithm changes the comfort zone linearly. The proposed algorithm, Signature Chaos GOA (SCGOA), overcomes these limitations in several ways. Firstly, it constructs the initial population using correlations. Second, unlike existing methods, it specifies specific procedures for initial and final iterations. After the initial iteration, the algorithm adjusts the comfort zone parameters dynamically using chaos theory and fuzzy signatures. Lastly, SCGOA aims to optimize both Support Vector Machine (SVM) parameters and feature subsets simultaneously. Objective functions include classification error, the proportion of selected features, and redundancy. In addition, different algorithms such as the Firefly Algorithm (FA), the Bat Algorithm (BA), and the Particle swarm Optimization (PSO) are compared. In comparison with FA, BA, PSO, and GOA, the proposed algorithm can improve the objective function by 30.6%, 34.9%, 7.6%, and 33.3%, respectively.

关键词： Grasshopper optimization Feature selection Fuzzy signature swarm algorithms

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An optimisation algorithm based on the behaviour of locust swarms

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2015年第6期7卷 402-407页

作者： Cuevas, Erik Gonzalez, Adrian Zaldivar, Daniel Perez-Cisneros, Marco Univ Guadalajara CUCEI Dept Elect Guadalajara 44430 Jalisco Mexico

In this paper, a novel swarm algorithm called locust search (LS) is proposed for solving optimisation tasks. The LS algorithm is based on the simulation of the behaviour presented in swarms of locusts. In the proposed algorithm, individuals emulate a group of locusts which interact to each other based on the biological laws of the cooperative swarm. Experimental results demonstrate a high performance of the proposed method for searching a global optimum in comparison with other well-known evolutionary methods.

关键词： swarm algorithms global optimisation bio-inspired algorithms

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A new algorithm inspired in the behavior of the social-spider for constrained optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第2期41卷 412-425页

作者： Cuevas, Erik Cienfuegos, Miguel Univ Guadalajara Dept Elect CUCEI Guadalajara 44430 Jalisco Mexico

During the past decade, solving constrained optimization problems with swarm algorithms has received considerable attention among researchers and practitioners. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. The SSO-C algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. For constraint handling, the proposed algorithm incorporates the combination of two different paradigms in order to direct the search towards feasible regions of the search space. In particular, it has been added: (1) a penalty function which introduces a tendency term into the original objective function to penalize constraint violations in order to solve a constrained problem as an unconstrained one;(2) a feasibility criterion to bias the generation of new individuals toward feasible regions increasing also their probability of getting better solutions. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. Simulation and comparisons based on several well-studied benchmarks functions and real-world engineering problems demonstrate the effectiveness, efficiency and stability of the proposed method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： swarm algorithms Constrained optimization Bio-inspired algorithms

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BeeRank: A Heuristic Ranking Model to Optimize the Retrieval Process

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INTERNATIONAL JOURNAL OF swarm INTELLIGENCE RESEARCH 2021年第2期12卷 39-56页

作者： Irfan, Shadab Dhanaraj, Rajesh Kumar Galgotias Univ Noida India

There is an incredible change in the world wide web, and the users face difficulty in accessing the needed information as per their need. Different algorithms are devised at each step of the information retrieval process, and it is observed that ranking is one of the core ingredients of any search engine that plays a major role in arranging the information. In this regard, different measures are adopted for ranking the web pages by using content, structure, or log data. The BeeRank algorithm is proposed that provides quality results, which is inspired by the artificial bee colony algorithm for web page ranking and uses both the structural and content approach for calculating the rank value and provides better results. It also helps the users in finding the relevant web pages by minimizing the computational complexity of the process and achieves the result in minimum time duration. The working is illustrated and is compared with the traditional PageRank algorithm that incorporates only structural links, and the result shows an improvement in ranking and provides user-specific results.

关键词： Artificial Bee Colony Evolutionary algorithms Information Retrieval Information Retrieval Models NatureInspired Algorithm Ranking Algorithm swarm algorithms Web Mining

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CUDA implementation of the antlion optimization algorithm

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2023年第2期38卷 118-139页

作者： Davendra, Donald Metlicka, Magdalena Bialic-Davendra, Magdalena Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA Siemens Sro Brno Czech Republic Cent Washington Univ Dept Finance & Econ Ellensburg WA USA

A parallel version of Ant Lion Optimizer algorithm using the CUDA platform is introduced in this paper. Efficient kernel, memory and thread management approaches have been developed to maximize its performance. The new algorithm was tested against the canonical algorithm on 15 scalable problems of different sizes, with a total of 172 experiments. The solution costs and the execution times were compared using relative percentage difference and significance tests. The results showed the CUDA antlion algorithm significantly improves upon the execution time, while retaining the same solution quality.

关键词： Antlion optimization CUDA parallel computing high performance computing swarm algorithms

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A Review on 2-D Supersonic Aerofoil Shape Optimization

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2024年第11期64卷 2559-2570页

作者： Vijayaparkavan, P. Lewise, K. Anton Savio Raj, R. Ajith Karunya Inst Technol & Sci Div Aerosp Engn Coimbatore 641114 Tamilnadu India

Review on various methods of optimization of the aerofoil for the supersonic aircraft is the main subject of this paper. The standard forms of the optimization problem are presented and various techniques for solving the resulting problem depends on the specific application are discussed. A great many optimization techniques exist and it is not possible to provide a complete review in the limited space available here. Rather, an attempt is made to focus on methods that are frequently applied in applications of engineering optimization. The benefits and drawbacks of the various approaches are outlined, and recommendations are provided to help the designer choose the best approach for the specific problem at hand. Where possible, a short figure of a respective optimized aerofoil is presented to aid the discussion of that particular optimization technique. The fundamental theory of supersonic flow is provided.

关键词： supersonic airfoils computational fluid dynamics optimization aerodynamic shape efficiency swarm algorithms

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A REVIEW ON THE SIMULATION OF SOCIAL NETWORKS INSIDE HEURISTIC algorithms 32

A REVIEW ON THE SIMULATION OF SOCIAL NETWORKS INSIDE HEURIST...

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32nd European Conference on Modelling and Simulation (ECMS)

作者： Senkerik, Roman Pluhacek, Michal Viktorin, Adam Kadavy, Tomas Janostik, Jakub Oplatkova, Zuzana Kominkova Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9780993244070

This paper represents a comprehensive review of selected methods for visualization of the population dynamics of the swarm and evolutionary algorithms in the form of networks. The whole idea is based on the obvious similarity between interactions between individuals in a swarm and evolutionary algorithms and for example, users of social networks, society, etc. The analogy between individuals from the population used in an arbitrary evolutionary or swarm-based algorithm and vertices (nodes) of a network is discussed here, as well as between edges in a network and communication between individuals in a population. Simple experiments with four well-known heuristic algorithms are described here, giving an insight into different approaches to the building of the network during metaheuristic run.

关键词： Social networks Graphs Analysis Evolutionary algorithms swarm algorithms

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