Ant colony system with communication strategies
TL;DR: Experimental results demonstrate the proposed ACS with communication strategies are superior to the existing ant colony system (ACS) and ant system (AS) with similar or better running times.
About: This article is published in Information Sciences.The article was published on 2004-12-02. It has received 188 citations till now. The article focuses on the topics: Ant colony & Ant colony optimization algorithms.
Citations
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Journal Article•
TL;DR: Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).
Abstract: In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).
496 citations
Cites background from "Ant colony system with communicatio..."
...By studying the behavior of ants achieves ACO, and with examining the movements of the flocking gulls realizes PSO....
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...Genetic Algorithm (GA) [1-2], Ant Colony Optimization (ACO) [6-7], Particle Swarm Optimization (PSO) [3-5], and Simulated Annealing (SA) [8-9] etc....
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...In the field of optimization, many algorithms were being proposed recent years, e.g. Genetic Algorithm (GA) [1-2], Ant Colony Optimization (ACO) [6-7], Particle Swarm Optimization (PSO) [3-5], and Simulated Annealing (SA) [8-9] etc....
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...Cat Swarm Optimization (CSO), the algorithm we proposed in this paper, is motivated from PSO [3] and ACO [6]....
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...For example, GA uses chromosome to represent the solution set; ACO uses ant as the agent, and the paths made by the ants depict the solution sets; PSO uses the positions of particles to delineate the solution sets....
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07 Aug 2006
TL;DR: Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).
Abstract: In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).
316 citations
Cites background from "Ant colony system with communicatio..."
...Genetic Algorithm (GA) [1-2], Ant Colony Optimization (ACO) [6-7], Particle Swarm Optimization (PSO) [3-5], and Simulated Annealing (SA) [8-9] etc....
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01 Jan 2007
TL;DR: A new optimization algorithm, namely, Cat Swarm Optimization (CSO) is proposed, which is generated by observing the behavior of cats, and composed of two sub-models by simulating thebehavior of cats.
Abstract: Optimization problems are very important in many fields To the present, many optimization algorithms based on computational intelligence have been proposed, such as the Genetic Algorithm, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) In this paper, a new optimization algorithm, namely, Cat Swarm Optimization (CSO) is proposed CSO is generated by observing the behavior of cats, and composed of two sub-models by simulating the behavior of cats According to the experiments, the results reveal that CSO is superior to PSO
274 citations
TL;DR: In this paper, a comprehensive review on the optimal allocation of distributed generators was carried out for different objectives, constraints, and algorithms, highlighting how the methods and algorithms for optimal distributed generation allocation play an important role in improving the accuracy and efficiency of the results.
Abstract: Distributed generation, with respect to its ability in utilizing the alternative resources of energy, provides a promising future for power generation in electric networks. Distributed generators contribution to power systems include improvement in energy efficiency and power quality to reliability and security. These benefits are only achievable with optimal allocation of distributed resources that considers the objective function, constraints, and employs suitable optimization algorithm. In this paper, a comprehensive review on the optimal allocation of distributed generators was carried out for different objectives, constraints, and algorithms. Current review highlights how the methods and algorithms for optimal distributed generation allocation play an important role in improving the accuracy and efficiency of the results.
262 citations
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.
254 citations
References
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01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
11,224 citations
"Ant colony system with communicatio..." refers background or methods in this paper
...Swarm intelligence research originates from work into the simulation of the emergence of collective intelligent behaviors of real ants....
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...Keywords: Ant colony system (ACS); Communication strategies; Ant system (AS)...
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...This algorithm is referred to as ant system (AS) algorithm....
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TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
Abstract: This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
7,596 citations
TL;DR: An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented.
Abstract: This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.
2,862 citations
"Ant colony system with communicatio..." refers background in this paper
...Keywords: Ant colony system (ACS); Communication strategies; Ant system (AS)...
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Proceedings Article•
01 Jan 1992
TL;DR: A distributed problem solving environment is introduced and its use to search for a solution to the travelling salesman problem is proposed.
Abstract: Ants colonies exhibit very interesting behaviours: even if a single ant only has simple capabilities, the behaviour of a whole ant colony is highly structured. This is the result of coordinated interactions. But, as communication possibilities among ants are very limited, interactions must be based on very simple flows of information. In this paper we explore the implications that the study of ants behaviour can have on problem solving and optimization. We introduce a distributed problem solving environment and propose its use to search for a solution to the travelling salesman problem.
2,826 citations
"Ant colony system with communicatio..." refers background in this paper
...Swarm intelligence research originates from work into the simulation of the emergence of collective intelligent behaviors of real ants....
[...]
...Keywords: Ant colony system (ACS); Communication strategies; Ant system (AS)...
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TL;DR: Through a human centered design project focused on an information science problem, students will gain experience and a better understanding of the process to develop an innovative solution addressing a societal need.
Abstract: Courses IS 100 Exploring the iSchool with a Human-Centered Lens credit: 1 Hour. (https://courses.illinois.edu/schedule/terms/IS/100/) This course introduces students to the School of Information Sciences (iSchool). Students will explore career and professional development within information sciences, building their leadership and collaborative skills, and building a network within and beyond the iSchool. Through a human centered design project focused on an information science problem, students will gain experience and a better understanding of the process to develop an innovative solution addressing a societal need. Prerequisite: Restricted to Majors Only; First Semester Freshman, Intercollegiate and Off-Campus Transfer Students Only.
1,029 citations