Showing papers on "Swarm intelligence published in 2008"
TL;DR: This paper discusses natural biogeography and its mathematics, and then discusses how it can be used to solve optimization problems, and sees that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO).
Abstract: Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems. Just as the mathematics of biological genetics inspired the development of genetic algorithms (GAs), and the mathematics of biological neurons inspired the development of artificial neural networks, this paper considers the mathematics of biogeography as the basis for the development of a new field: biogeography-based optimization (BBO). We discuss natural biogeography and its mathematics, and then discuss how it can be used to solve optimization problems. We see that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO). This makes BBO applicable to many of the same types of problems that GAs and PSO are used for, namely, high-dimension problems with multiple local optima. However, BBO also has some features that are unique among biology-based optimization methods. We demonstrate the performance of BBO on a set of 14 standard benchmarks and compare it with seven other biology-based optimization algorithms. We also demonstrate BBO on a real-world sensor selection problem for aircraft engine health estimation.
3,418 citations
01 Jan 2008
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Abstract: Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.
3,242 citations
TL;DR: This paper presents a detailed overview of the basic concepts of PSO and its variants, and provides a comprehensive survey on the power system applications that have benefited from the powerful nature ofPSO as an optimization technique.
Abstract: Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.
2,147 citations
TL;DR: This paper shows how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure, and compares the results with those reported in the literature for other continuous optimization methods.
1,238 citations
Book•
01 Jan 2008
TL;DR: This paper presents a meta-modelling framework for Swarm Intelligence and Collective Robotics that automates the very labor-intensive and therefore time-heavy and expensive process of designing and deploying swarm-bots.
Abstract: Keywords: Swarm Intelligence ; Collective Robotics ; SWARM_BOTS ; Evolutionary Robotics Note: Proceedings of the ANTS 2004, 4th International Workshop Sponsor: swarm-bots, OFES 01-0012-1 Reference LIS-BOOK-2004-002 URL: http://iridia.ulb.ac.be/~ants/ants2004/ Record created on 2006-01-12, modified on 2017-05-10
443 citations
22 Sep 2008
TL;DR: An adaptive particle swarm optimization with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach is proposed, resulting in substantially improved quality of global solutions.
Abstract: This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an `evolutionary factor' by using the population distribution information and relative particle fitness information in each generation, and estimates the evolutionary state through a fuzzy classification method. According to the identified state and taking into account various effects of the algorithm-controlling parameters, adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. Further, an adaptive `elitist learning strategy' (ELS) is designed for the best particle to jump out of possible local optima and/or to refine its accuracy, resulting in substantially improved quality of global solutions. The APSO algorithm is tested on 6 unimodal and multimodal functions, and the experimental results demonstrate that the APSO generally outperforms the compared PSOs, in terms of solution accuracy, convergence speed and algorithm reliability.
442 citations
TL;DR: This chapter focuses on two of the most successful examples of optimization techniques inspired by swarm intelligence: ant colony optimization and particle swarm optimization.
Abstract: Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems. In this chapter we focus on two of the most successful examples of optimization techniques inspired by swarm intelligence: ant colony optimization and particle swarm optimization. Ant colony optimization was introduced as a technique for combinatorial optimization in the early 1990s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. In addition, particle swarm optimization was introduced for continuous optimization in the mid-1990s, inspired by bird flocking.
389 citations
TL;DR: A novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed, and experimental results show that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimize RBFNN.
Abstract: In this paper, a novel heuristic structure optimization methodology for radial basis probabilistic neural networks (RBPNNs) is proposed. First, a minimum volume covering hyperspheres (MVCH) algorithm is proposed to select the initial hidden-layer centers of the RBPNN, and then the recursive orthogonal least square algorithm (ROLSA) combined with the particle swarm optimization (PSO) algorithm is adopted to further optimize the initial structure of the RBPNN. The proposed algorithms are evaluated through eight benchmark classification problems and two real-world application problems, a plant species identification task involving 50 plant species and a palmprint recognition task. Experimental results show that our proposed algorithm is feasible and efficient for the structure optimization of the RBPNN. The RBPNN achieves higher recognition rates and better classification efficiency than multilayer perceptron networks (MLPNs) and radial basis function neural networks (RBFNNs) in both tasks. Moreover, the experimental results illustrated that the generalization performance of the optimized RBPNN in the plant species identification task was markedly better than that of the optimized RBFNN.
359 citations
TL;DR: Computational results show that simultaneously taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.
Abstract: This paper aims to study distribution system operations by the ant colony search algorithm (ACSA). The objective of this study is to present new algorithms for solving the optimal feeder reconfiguration problem, the optimal capacitor placement problem, and the problem of a combination of the two. The ACSA is a relatively new and powerful swarm intelligence method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as ldquogreedyrdquo search. The ACSA was inspired from the natural behavior of ants in locating food sources and bring them back to their colony by the formation of unique trails. Therefore, through a collection of cooperative agents called ldquoants,rdquo the near-optimal solution to the feeder reconfiguration and capacitor placement problems can be effectively achieved. In addition, the ACSA applies the state transition, local pheromone-updating, and global pheromone-updating rules to facilitate the computation. Through simultaneous operation of population agents, process stagnation can be effectively prevented. Optimization capability can be significantly enhanced. The proposed approach is demonstrated using two example systems from the literature. Computational results show that simultaneously taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.
319 citations
Book•
01 Oct 2008TL;DR: The first part of this book is intended to provide an overview of swarm intelligence to novices, and to offer researchers in the field an update on interesting recent developments, while the second part contains chapters on more specific topics of swarm research such as the evolution of robot behavior, the use of particle swarms for dynamic optimization, and organic computing.
Abstract: The laws that govern the collective behavior of social insects, flocks of birds, or fish schools continue to mesmerize researchers. While individuals are rather unsophisticated, in cooperation they can solve complex tasks, a prime example being the ability of ant colonies to find shortest paths between their nests and food sources. Task-solving results from self-organization, which often evolves from simple means of communication, either directly or indirectly via changing the environment, the latter referred to as stigmergy. Scientists have applied these principles in new approaches, for example to optimization and the control of robots. Characteristics of the resulting systems include robustness and flexibility. This field of research is now referred to as swarm intelligence. The contributing authors are among the top researchers in their domain. The book is intended to provide an overview of swarm intelligence to novices, and to offer researchers in the field an update on interesting recent developments. Introductory chapters deal with the biological foundations, optimization, swarm robotics, and applications in new-generation telecommunication networks, while the second part contains chapters on more specific topics of swarm intelligence research such as the evolution of robot behavior, the use of particle swarms for dynamic optimization, and organic computing.
287 citations
TL;DR: The techniques covered are case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems.
TL;DR: Improved PSO approaches for solving EDPs that takes into account nonlinear generator features such as ramp-rate limits and prohibited operating zones in the power system operation are proposed.
TL;DR: This paper presents a hybrid algorithm combining ant colony optimization algorithm with the taboo search algorithm for the classical job shop scheduling problem, which employs a novel decomposition method inspired by the shifting bottleneck procedure, and a mechanism of occasional reoptimizations of partial schedules.
01 Nov 2008
TL;DR: An ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks and a composite reliability index is used as the objective function in the optimization procedure.
Abstract: Optimal placement of protection devices and distributed generators (DGs) in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks. A composite reliability index is used as the objective function in the optimization procedure. Simulations are carried out based on two practical distribution systems to validate the effectiveness of the proposed method. Furthermore, comparative studies in relation to genetic algorithm are also conducted.
TL;DR: This paper proposes a novel tuning strategy for robust proportional-integral-derivative (PID) controllers based on the augmented Lagrangian particle swarm optimization (ALPSO) and is evaluated by several simulation examples, which demonstrate that the proposed approach works well to obtain PID controller parameters satisfying the multiple H ∞ performance criteria.
TL;DR: This work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator, and demonstrates good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design.
Abstract: Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel Quantum-behaved PSO (QPSO) using chaotic mutation operator. The application of chaotic sequences based on chaotic Zaslavskii map instead of random sequences in QPSO is a powerful strategy to diversify the QPSO population and improve the QPSO’s performance in preventing premature convergence to local minima. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design.
TL;DR: A novel three-state approach inspired from the discrete version of a powerful heuristic algorithm, particle swarm optimization, is developed and presented to determine the optimum number and locations of two types of switches in radial distribution systems.
Abstract: Achieving high-distribution reliability levels and concurrently minimizing capital costs can be considered as the main issues in distribution system optimization Determination of the optimum number and location of switches in distribution system automation is an important issue from the reliability and economical points of view In this paper, a novel three-state approach inspired from the discrete version of a powerful heuristic algorithm, particle swarm optimization, is developed and presented to determine the optimum number and locations of two types of switches (sectionalizers and breakers) in radial distribution systems The novelty of the proposed algorithm is to simultaneously consider both sectionalizer and breaker switches The feasibility of the proposed algorithm is examined by application to two distribution systems The proposed solution approach provides a global optimal solution for the switch placement problem
TL;DR: In this article, a Mixed-Model U-shaped Disassembly Line with Stochastic Task Times has been proposed, which simultaneously tackles the interrelated problem of line balancing and model sequencing.
Abstract: Disassembly operations are inevitable elements of product recovery with the disassembly line as the best choice to carry out the same. In the light of different structures of returned products (models) and variations in task completion times, the process of disassembly could not be efficiently mapped on a simple straight line. Another important issue that needs consideration is the task-time variability pertaining to human factor. In order to resolve these complexities a Mixed-Model U-shaped Disassembly Line with Stochastic Task Times has been proposed in this article. A novel approach, Collaborative Ant Colony Optimization (CACO), has been utilized that simultaneously tackles the interrelated problem of line balancing and model sequencing. The distinguishing feature of the proposed approach is that it maintains bilateral colonies of ants which independently identifies the two sequences, but utilizes the information obtained by their collaboration to guide the future path. The approach is tested on benchm...
TL;DR: A fuzzy adaptive PSO (FAPSO) is proposed to improve the performance of PSO, a new formulation of multi-objective reactive power and voltage control for power system with promising numerical results of the IEEE 30-bus and IEEE 118-bus power systems.
01 Oct 2008
TL;DR: This paper introduces a novel approach for searching in high-dimensional spaces taking into account behaviors drawn from fish schools, and presents simulations where the FSS algorithm is compared with, and in some cases outperforms, well-known intelligent algorithms such as particle swarm optimization inHigh-dimensional searches.
Abstract: Search problems are sometimes hard to compute. This is mainly due to the high dimensionality of some search spaces. Unless suitable approaches are used, search processes can be time-consuming and ineffective. Nature has evolved many complex systems able to deal with such difficulties. Fish schools, for instance, benefit greatly from the large number of constituent individuals in order to increase mutual survivability. In this paper we introduce a novel approach for searching in high-dimensional spaces taking into account behaviors drawn from fish schools. The derived algorithm - fish-school search (FSS) - is mainly composed of three operators: feeding, swimming and breeding. Together these operators afford the evoked computation: (i) wide-ranging search abilities, (ii) automatic capability to switch between exploration and exploitation, and (iii) self-adaptable global guidance for the search process. This paper includes a detailed description of the novel algorithm. Finally, we present simulations where the FSS algorithm is compared with, and in some cases outperforms, well-known intelligent algorithms such as particle swarm optimization in high-dimensional searches.
12 Mar 2008
TL;DR: A novel and computationally efficient global optimization method based on swarm intelligence for locating nodes in a WSN environment using the mean squared range error of all neighbouring anchor nodes is taken as the objective function.
Abstract: This paper proposes a novel and computationally efficient global optimization method based on swarm intelligence for locating nodes in a WSN environment The mean squared range error of all neighbouring anchor nodes is taken as the objective function for this non linear optimization problem The Particle Swarm Optimization (PSO) is a high performance stochastic global optimization tool that ensures the minimization of the objective function, without being trapped into local optima The easy implementation and low memory requirement features of PSO make it suitable for highly resource constrained WSN environments Computational experiments on data drawn from simulated WSNs show better convergence characteristics than the existing Simulated Annealing based WSN localization
TL;DR: The primary goal of this paper is to acquaint readers with the basic principles of Swarm Intelligence, as well as to indicate potential swarm intelligence applications in traffic and transportation.
Abstract: Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual “agents” and that each agent interacts with other agents according to localized knowledge. Special kinds of artificial agents are the agents created by analogy with social insects. Social insects (bees, wasps, ants, and termites) have lived on Earth for millions of years. Their behavior is primarily characterized by autonomy, distributed functioning, and self-organizing capacities. Social insect colonies teach us that very simple organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. Swarm intelligence is the branch of artificial intelligence based on study of behavior of individuals in various decentralized systems. The paper presents a classification and analysis of the results achieved using swarm intelligence (SI) to model complex traffic and transportation processes. The primary goal of this paper is to acquaint readers with the basic principles of Swarm Intelligence, as well as to indicate potential swarm intelligence applications in traffic and transportation.
01 Jan 2008
TL;DR: A new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization is described, which may come with a variety of attributes or features.
Abstract: Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.
TL;DR: A new particle swarm optimization (PSO) for the open shop scheduling problem is presented, compared with the original PSO, and a modified parameterized active schedule generation algorithm (mP-ASG) is implemented to decode a particle position into a schedule.
TL;DR: The anti-predatory activity is modeled and embedded in the classical PSO to form APSO, which enhances the exploration capability of the swarm and is applied to two test systems having nonconvex solution spaces.
TL;DR: In this article, the authors considered an n-job, m-machine lot-streaming problem in a flow shop with equal-size sublots where the objective is to minimize the total weighted earliness and tardiness.
TL;DR: A multiobjective two-dimensional mathematical model for bin packing problems with multiple constraints (MOBPP-2D) is formulated and the concept of Pareto's optimality is incorporated to evolve a family of solutions along the trade-off surface.
TL;DR: Two swarm intelligence control mechanisms used for distributed robot path formation, called vectorfield and chains, are presented and it is observed that chains perform better for small robot group sizes, while vectorfield performs better for large groups.
Abstract: We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. The second mechanism is called vectorfield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location.
TL;DR: Experimental results show the advantages of BAS over other ACO-based approaches for the benchmark problems selected from OR library, including the pheromone laying method specially designed for the binary solution structure.
01 Mar 2008
TL;DR: A computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed and is compared with other approaches reported in some existing literature works.
Abstract: The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach.