Papers by Keyword: Swarm Intelligence

Abstract: In the present world, mobile robot has been widely used for many functions across different areas of life. These mobile robots can be engaged in a static or dynamic environment where they are expected to accomplish a task optimally against all odds. Path planning for mobile robot is a very crucial problem in robotics that has been greatly researched upon; it is aimed at finding an optimal path in a given environment from a start point to the goal point. Several techniques have been employed in solving this crucial problem. These techniques are broadly classified as classical and heuristics. The Swarm Intelligence Techniques form a sub-class of the heuristics approach. The aim of this research is to review the swarm intelligence techniques in solving the mobile robot path planning problem. The drawbacks and merits of each of the techniques were discussed and a comparative analysis was given.

Abstract: Particle swarm optimization (PSO) is a heuristic global optimization method. PSO was motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and the ability to adapt various dynamic environments, makes PSO one of the most important swarm intelligence algorithms and ostensibly the most commonly used optimization technique. This survey presents a comprehensive investigation of PSO and in particular, a proposed theoretical framework to improve its implementation. We hope that this survey would be beneficial to researchers studying PSO algorithms and would also serve as the substratum for future research in the study area, particularly those pursuing their career in artificial intelligence. In the end, some important conclusions and possible research directions of PSO that need to be studied in the future are proposed.

Abstract: This paper built a multi-objective optimization model and proposed an improved multi-objective particle swarm optimization algorithm called MPS²O ,which is based on Multiple Particle Swarm Co-evolutionary. The MPS²O algorithm has considerable potential for solving multi-objective optimization problems. Mathematical benchmark functions also shows that the proposed algorithm is an excellent Alternative for solving multi-objective optimization problems. Making full use of the research findings home and abroad, MPS²O has been chosen to be the coverage optimization strategy of the wireless sensor networks in Water Environment Monitoring System. Simulation results demonstrate that the MPS²O algorithm is more efficient than the PSO algorithm in solving this real-world problem.

Abstract: Basic Artificial Fish Swarm(AFS) Algorithm is a new type of heuristic swarm intelligence algorithm but optimization is difficult to get a very high precision due to the randomness of the artificial fish behavior.This paper presents an extended AFS algorithm, namely the Cooperative Artificial Fish Swarm (CAFS),which significantly improves the original AFS in solving complex optimization problems. In this work,firstly,CAFS algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by CAFS, Particle Swarm Optimization (PSO) are studied.Secondly,K-medoids and CAFS algorithm is used for data clustering on several benchmark data sets.The simulation results show that the proposed CAFS outperforms the other two algorithms in terms of accuracy,robustness and convergence speed.

Abstract: Harmony search (HS) algorithm is a new population based algorithm, which imitates the phenomenon of musical improvisation process. Its own potential and shortage, one shortage is that it easily trapped into local optima. In this paper, a hybrid harmony search algorithm (HHS) is proposed based on the conception of swarm intelligence. HHS employed a local search method to replace the pitch adjusting operation, and designed an elitist preservation strategy to modify the selection operation. Experiment results demonstrated that the proposed method performs much better than the HS and its improved algorithms (IHS, GHS and NGHS).

Abstract: A new design of automated guided vehicles control system is presented, which applies wireless sensor network (WSN) communication technology to make vehicles work as mobile robots with distributed sensing ability, meanwhile uses swarm intelligence algorithms to coordinate the vehicles. Compared to the traditonal system, this system is more flexible because that it is an infrastructure-free system with WSN to assist locating and navigating, and it is suitable for the dynamic industrial applications with self-organization network communication and swarm intelligence control.

Abstract: The paper presents an application of Ant Colony Optimization algorithm as a partof procedure for solving the inverse solidification problem. Investigated task consists in recon-struction of the boundary condition (heat flux) on the basis of temperature measurements inselected points of the cast. First step of the method is based on the finite difference method withapplication of the generalized alternating phase truncation method and serves for solving theappropriate direct solidification problem. In the second step some functional representing thecrucial part of the procedure is minimized with the aid of Ant Colony Optimization algorithm.

Abstract: Aim of the paper is to solve the inverse problem in solidification of binary alloyby applying the Artificial Bee Colony algorithm. Considered inverse problem consists in recon-struction of the heat flux and the distribution of temperature in case when the temperaturemeasurements in selected points of the alloy are known and is mathematically modeled be meansof the heat conduction equation with the substitute thermal capacity and with the liquidus andsolidus temperatures varying in dependence on the concentration of the alloy component. Fordescribing the concentration the lever arm model is applied and for minimizing a functional,constituting the crucial part of the procedure, the ABC algorithm is used.

Abstract: In order to resolve these problems, we put forward a new design of the intelligent lock which is mainly based on the technology of wireless sensor network. Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems; it easily falls into local optima when solving complex multimodal problems. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence. The paper gives the circuit diagram of the hardware components based on single chip and describe how to design the software. The experimental results show that the immune genetic algorithm based on chaos theory can search the result of the optimization and evidently improve the convergent speed and astringency.

Abstract: Data mining technology based on the particle swarm optimization algorithm applied in earthquake prediction was presented. Making use of the characteristics of abnormally high-dimensional data of earthquake precursor, this paper studies an earthquake prediction model based on the Particle Swarm Optimization Clustering Algorithm. This model analyzes the relationship between earthquake precursor data and earthquake magnitude. Inputs are 14 abnormal indexes such as belt, seismic gap and short leveling, and output is earthquake magnitude classification. The cluster average-distance is set as the evaluation function of the Particle Swarm Optimization Algorithm. The experimental results indicate that, this model can effectively and validly predict the earthquake magnitude in accordance with the earthquake precursor data. Compared with the traditional clustering k-means Algorithm model, this stability is stronger, and the correctness of forecast is much higher. Through the research and analysis of the example of history source seismic data, the model of this paper is one of approaches to improve the efficiency of earthquake forecast.