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A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

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OPTIMIZATION AND ENGINEERING 2017年第3期18卷 659-691页

作者： Poole, Daniel J. Allen, Christian B. Rendall, Thomas C. S. Univ Bristol Dept Aerosp Engn Bristol BS8 1TR Avon England

A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers;particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.

关键词： Optimization Constraint handling swarm algorithms Aerodynamic shape optimization

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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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Shuffled Multi-Population Bat Algorithm (SMPBat)

Shuffled Multi-Population Bat Algorithm (SMPBat)

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci Delhi India Univ Delhi Dyal Singh Coll Delhi India

ISBN: (纸本)9781509063673

Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimisation problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different hybrids of BA. This paper proposes Shuffled Multi Population Bat algorithm (SMPBat), a hybrid between two recently proposed variants of BA:- Enhanced Shuffled Bat algorithm (EShBAT) and Bat algorithm with Ring Master-Slave strategy (BatRM-S). BatRM-S is a multi-population variant of BA which partitions it's population according to a combination of the ring and master-slave strategies. EShBAT incorporates shuffling into BA. The proposed algorithm, SMPBat combines the population partitioning strategies of these two algorithms to enhance the exploitation and exploration capabilities of BA. The evolution strategy of SMPBat also strives to retain the improved solutions. The standard BA replaces a solution with a new solution around the best. However, in this process, the information gained by that solution so far is completely lost. SMPBat changes this exploitation technique used by BA. SMPBat is compared to BatRM-S, EShBAT and BA, over 30 well-known optimisation functions. Results establish SMPBat as a significant improvement over BA, EShBAT and BatRM-S.

关键词： Bat algorithm Master-Slave strategy Multi Population algorithms swarm algorithms Numerical optimisation

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On Evolutionary Dynamics Modeled by Ant Algorithm 8

On Evolutionary Dynamics Modeled by Ant Algorithm

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8th International Conference on Intelligent Networking and Collaborative Systems (INCoS)

作者： Zelinka, Ivan Tomaszek, Lukas Kojecky, Lumir VSB Tech Univ Ostrava Dept Comp Sci FEI Tr 17 Listopadu 15 Ostrava Czech Republic

ISBN: (纸本)9781509041237

In this paper we are continuing in our research to show mutual intersection of two different areas of research: complex network and evolutionary computation. This research parer is focused on possibility to convert run of evolution algorithm to a complex network inspired by ants. Such network can then be analyzed to get useful information about algorithm dynamics. In this paper we focused on one global network property, average network strength. This property is described and used for different types of cost functions and different representations of network conversion.

关键词： Complex networks Evolutionary algorithms SOMA algorithm swarm algorithms

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Multiobjective Small-World Optimization for Energy Saving in Grid Environments

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COMPUTER JOURNAL 2015年第3期58卷 432-447页

作者： Arsuaga-Rios, Maria Vega-Rodriguez, Miguel A. CERN European Org Nucl Res Beams Dept CH-1211 Geneva 23 Switzerland Univ Extremadura Escuela Politecn Dept Technol Comp & Commun ARCO Res Grp Caceres 10003 Spain

Job scheduling is a challenging task in grid environments and reducing execution time is one of the most important ways to tackle this problem. Nowadays, society is also more conscious about energy saving, making this fact a really important issue to pursue. In this paper, a new multiobjective algorithm is presented based on the small-world phenomenon: Multiobjective Small-World Optimization (MOSWO), to optimize both objectives: energy consumption and execution time. MOSWO is compared with another recent and swarm algorithm based on the firefly's behaviour: Multiobjective Firefly Algorithm (MO-FA). Both algorithms are compared with the standard multiobjective algorithm Non-dominated Sorting Genetic Algorithm II to show their efficiency as multiobjective approaches. Moreover, the best algorithm proposed, MOSWO, is compared with MOHEFT (multiobjective version of one of the most used algorithms in workflow-scheduling, HEFT) and also with two real grid schedulers: Workload Management System and Deadline Budget Constraint. The results show the advantages of our proposal.

关键词： multiobjective optimization grid environments swarm algorithms energy saving execution time small-world

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

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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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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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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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Bare Bones Particle swarm Optimization With Scale Matrix Adaptation

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第9期44卷 1567-1578页

作者： Campos, Mauro Krohling, Renato A. Enriquez, Ivan Univ Fed Espirito Santo Dept Stat BR-29075910 Vitoria ES Brazil Univ Fed Espirito Santo Dept Prod Engn BR-29075910 Vitoria ES Brazil Univ Fed Espirito Santo Grad Program Comp Sci BR-29075910 Vitoria ES Brazil

Bare bones particle swarm optimization (BBPSO) is a swarm algorithm that has shown potential for solving single-objective unconstrained optimization problems over continuous search spaces. However, it suffers of the premature convergence problem that means it may get trapped into a local optimum when solving multimodal problems. In order to address this drawback and improve the performance of the BBPSO, we propose a variant of this algorithm, named by us as BBPSO with scale matrix adaptation (SMA), SMA-BBPSO for short reference. In the SMA-BBPSO, the position of a particle is selected from a multivariate t-distribution with a rule for adaptation of its scale matrix. We use the multivariate t-distribution in its hierarchical form, as a scale mixtures of normal distributions. The t-distribution has heavier tails than those of the normal distribution, which increases the ability of the particles to escape from a local optimum. In addition, our approach includes the normal distribution as a particular case. As a consequence, the t-distribution can be applied during the optimization process by maintaining the proper balance between exploration and exploitation. We also propose a simple update rule to adapt the scale matrix associated with a particle. Our strategy consists of adapting the scale matrix of a particle such that the best position found by any particle in its neighborhood is sampled with maximum likelihood in the next iteration. A theoretical analysis was developed to explain how the SMA-BBPSO works, and an empirical study was carried out to evaluate the performance of the proposed algorithm. The experimental results show the suitability of the proposed approach in terms of effectiveness to find good solutions for all benchmark problems investigated. Nonparametric statistical tests indicate that SMA-BBPSO shows a statistically significant improvement compared with other swarm algorithms.

关键词： Multivariate t-distribution scale matrix adaptation (SMA) scale mixtures of normal distributions swarm algorithms

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Neighborhood Research Approach in swarm Intelligence for Solving the Optimization Problems 12

Neighborhood Research Approach in Swarm Intelligence for Sol...

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Proceedings of IEEE East West Design and Test Symposium ( EWDTS 2014)

作者： Kuliev, E. V. Kureychik, V. V. Dukkardt, A. N. Legebokov, A. A. Southern Fed Univ Coll Automat & Comp Sci Rostov Na Donu Russia

ISBN: (纸本)9781479976300

The article discusses the key problem of swarm algorithms and the bioinspired approach, which is to determine the proximity function and study the emerging neighborhoods in order to solve optimization problems. There is a detailed discussion of one of the most important design phases, namely, the VLSI components placement problem, whose solutions fineness directly affects the quality of circuit tracing. The solution of the neighborhoods and solution proximity problem is demonstrated by the study of the solutions by means of hybrid search methods. The main idea of this approach is the sequential use of genetic and swarm algorithms. We propose a new formation principle of the positions' neighborhood in the solution space based on the bee colony algorithm, which uses the concept of neighborhood in a circular search space. There are also experimental studies which show that the time complexity of the developed approach does not go beyond polynomial dependence.

关键词： VLSI genetic algorithms integrated circuit layout particle swarm optimisation VLSI components placement problem circular search genetic algorithm hybrid search method neighborhood research neighborhood search optimization problems proximity function swarm algorithms swarm intelligence Algorithm design and analysis Educational institutions Genetic algorithms Search problems Sociology Statistics Very large scale integration Particle swarm optimization Very large scale integration Genetic Algorithm (GA) Algorithm design and analysis tabu search integrated circuit layout Educational institutions Sociology Neighborhood Search methods

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