Volume 2, 2011

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List of IJSIR published papers

Shi Cheng, Junfeng Chen, and Yuhui Shi June 2017

Volume 2, 2011

1. Gary G. Yen and Wen-Fung Leong. A Multiobjective Particle Swarm Optimizer for Constrained Optimization. International Journal of Swarm Intelligence Research (IJSIR) 2(1) (2011), 123. doi: 10.4018/jsir.2011010101.


Abstract: Constraint handling techniques are mainly designed for evolutionary algorithms to solve constrained multiobjective optimization problems (CMOPs). Most multiojective particle swarm optimization (MOPSO) designs adopt these existing constraint handling techniques to deal with CMOPs. In the proposed constrained MOPSO, information related to particles infea-sibility and feasibility status is utilized eectively to guide the particles to search for feasible solutions and improve the quality of the optimal solution. This information is incorporated into the four main procedures of a standard MOPSO algorithm. The involved procedures include the updating of personal best archive based on the particles Pareto ranks and their constraint viola-tion values; the adoption of infeasible global best archives to store infeasible nondominated so-lutions; the adjustment of acceleration constants that depend on the personal bests and selected global bests infeasibility and feasibility status; and the integration of personal bests feasibility status to estimate the mutation rate in the mutation procedure. Simulation to investigate the proposed constrained MOPSO in solving the selected benchmark problems is conducted. The simulation results indicate that the proposed constrained MOPSO is highly competitive in solv-ing most of the selected benchmark problems.


2. Gomaa Zaki El-Far. Design of Robust Approach for Failure Detection in Dynamic Control Sys-tems. International Journal of Swarm Intelligence Research (IJSIR) 2(1) (2011), 2443. doi: 10 . 4018/jsir.2011010102.


Abstract: This paper presents a robust instrument fault detection (IFD) scheme based on mod-ied immune mechanism based evolutionary algorithm (MIMEA) that determines on line the optimal control actions, detects faults quickly in the control process, and recongures the con-troller structure. To ensure the capability of the proposed MIMEA, repeating cycles of crossover, mutation, and clonally selection are included through the sampling time. This increases the abil-ity of the proposed algorithm to reach the global optimum performance and optimize the con-troller parameters through a few generations. A fault diagnosis logic system is created based on the proposed algorithm, nonlinear decision functions, and its derivatives with respect to time. Threshold limits are implied to improve the system dynamics and sensitivity of the IFD scheme to the faults. The proposed algorithm is able to recongure the control law safely in all the situ-ations. The presented false alarm rates are also clearly indicated. To illustrate the performance of the proposed MIMEA, it is applied successfully to tune and optimize the controller parame-ters of the nonlinear nuclear power reactor such that a robust behavior is obtained. Simulation results show the eectiveness of the proposed IFD scheme based MIMEA in detecting and isolat-ing the dynamic system faults.


3. Prithviraj Dasgupta, Taylor Whipple, and Ke Cheng. Eects of Multi-Robot Team Formations on Distributed Area Coverage. International Journal of Swarm Intelligence Research (IJSIR) 2(1) (2011), 4469. doi: 10.4018/jsir.2011010103.


Abstract: This paper examines the problem of distributed coverage of an initially unknown en-vironment using a multi-robot system. Specically, focus is on a coverage technique for coordi-nating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams mov-ing in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the eect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of ob-stacles and walls on the performance of the area coverage operation.


4. Mahamed G. H. Omran. Guest editorial Preface Special Issue on Scatter Search and Path Relink-ing Methods. International Journal of Swarm Intelligence Research (IJSIR) 2(2) (2011), iii.


5. Rafael Mart´ı, Juan-Jos´e Pantrigo, Abraham Duarte, Vicente Campos, and Fred Glover. Scatter Search and Path Relinking : A Tutorial on the Linear Arrangement Problem. International Journal of Swarm Intelligence Research (IJSIR) 2(2) (2011), 121. doi: 10.4018/jsir.2011040101.


Abstract: Scatter search (SS) and path relinking (PR) are evolutionary methods that have been successfully applied to a wide range of hard optimization problems. The fundamental concepts and principles of the methods were rst proposed in the 1970s and 1980s, and were based on formulations, dating back to the 1960s, for combining decision rules and problem constraints. The methods use strategies for search diversication and intensication that have proved eec-tive in a variety of optimization problems and that have sometimes been embedded in other evo-lutionary methods to yield improved performance. This paper examines the scatter search and path relinking methodologies from both conceptual and practical points of view, and identies certain connections between their strategies and those adopted more recently by particle swarm optimization. The authors describe key elements of the SS & PR approaches and apply them to a hard combinatorial optimization problem: the minimum linear arrangement problem, which has been used in applications of structural engineering, VLSI and software testing.


6. Peng-Yeng Yin, Fred Glover, Manuel Laguna, and Jia-Xian Zhu. A Complementary Cyber Swarm Algorithm. International Journal of Swarm Intelligence Research (IJSIR) 2(2) (2011), 2241. doi: 10.4018/jsir.2011040102.


Abstract: A recent study (Yin et al., 2010) showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more eective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts dierent sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manip-ulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Ex-perimental results with a large set of challenging test functions show that the C/CyberSA out-performs two recently proposed swarm-based methods by nding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach fur-ther produces improvements comparable to those obtained by the original CyberSA in relation to the Standard PSO 2007 method (Clerc, 2008).


7. Volodymyr P. Shylo and Oleg V. Shylo. Path Relinking Scheme for the Max-Cut Problem within Global Equilibrium Search. International Journal of Swarm Intelligence Research (IJSIR) 2(2) (2011), 4251. doi: 10.4018/jsir.2011040103.


Abstract: In this paper, the potential of the path relinking method for the maximum cut prob-lem is investigated. This method is embedded within global equilibrium search to utilize the set of high quality solutions provided by the latter. The computational experiment on a set of stan-dard benchmark problems is provided to study the proposed approach. The empirical experi-ments reveal that the large sizes of the elite set lead to restart distribution of the running times, i.e., the algorithm can be accelerated by simply removing all of the accumulated data (set P) and re-initiating its execution after a certain number of elite solutions is obtained.


8. Tabitha James and Cesar Rego. Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem. International Journal of Swarm Intelligence Research (IJSIR) 2(2) (2011), 5270. doi: 10.4018/jsir.2011040104.


Abstract: This paper introduces a new path relinking algorithm for the well-known quadratic assignment problem (QAP) in combinatorial optimization. The QAP has attracted considerable attention in research because of its complexity and its applicability to many domains. The algo-rithm presented in this study employs path relinking as a solution combination method incor-porating a multistart tabu search algorithm as an improvement method. The resulting algorithm has interesting similarities and contrasts with particle swarm optimization methods. Computa-tional testing indicates that this algorithm produces results that rival the best QAP algorithms. The authors additionally conduct an analysis disclosing how dierent strategies prove more or less eective depending on the landscapes of the problems to which they are applied. This anal-ysis lays a foundation for developing more eective future QAP algorithms, both for methods based on path relinking and tabu search, and for hybrids of such methods with related processes found in particle swarm optimization.


9. Pawel Paduch and Krzysztof Sapiecha. How Ants Can Eciently Solve Generalized Watchman Route Problem. International Journal of Swarm Intelligence Research (IJSIR) 2(3) (2011), 115. doi: 10.4018/jsir.2011070101.


Abstract: This paper presents a new algorithm for solving the generalized watchman problem. It is the problem of mobile robot operators that must nd the shortest route for the robot to see the whole area with many obstructions. The algorithm adapts the well-known ant algorithm to the new problem. An experiment where the algorithm is applied to an area containing more than 10 obstructions is described. It proves that eciency and accuracy of the algorithm are high.


10. Hongwei Mo and Yujing Yin. Image Segmentation Based on Bacterial Foraging and FCM Algo-rithm. International Journal of Swarm Intelligence Research (IJSIR) 2(3) (2011), 1628. doi: 10 . 4018/jsir.2011070102.


Abstract: This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this eld. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capa-bility of FCM, the Bacterial Foraging Algorithm was employed to optimize the objective criterion function which is interrelated to centroids in FCM. To evaluate the validation of the composite algorithm, cluster validation indexes were used to obtain numerical results and guide the possi-ble best solution found by BF-FCM. Several experiments were conducted on three UCI data sets. For image segmentation, BF-FCM successfully segmented 8 typical grey scale images, and most of them obtained the desired eects. All the experiment results show that BF-FCM has better performance than that of standard FCM.


11. Jing Liu, Jinshu Li, Weicai Zhong, Li Zhang, and Ruochen Liu. Minimum Span Frequency As-signment Based on a Multiagent Evolutionary Algorithm. International Journal of Swarm Intelli-gence Research (IJSIR) 2(3) (2011), 2942. doi: 10.4018/jsir.2011070103.


Abstract: In frequency assignment problems (FAPs), separation of the frequencies assigned to the transmitters is necessary to avoid the interference. However, unnecessary separation causes an excess requirement of spectrum, the cost of which may be very high. Since FAPs are closely related to T-coloring problems (TCP), multiagent systems and evolutionary algorithms are combined to form a new algorithm for minimum span FAPs on the basis of the model of TCP, which is named as Multiagent Evolutionary Algorithm for Minimum Span FAPs (MAEA-MSFAPs). The objective of MAEA-MSFAPs is to minimize the frequency spectrum required for a given level of reception quality over the network. In MAEA-MSFAPs, all agents live in a lattice-like environment. Making use of the designed behaviors, MAEA-MSFAPs realizes the ability of agents to sense and act on the environment in which they live. During the process of interacting with the environment and other agents, each agent increases the energy as much as possible so that MAEA-MSFAPs can nd the optima. Experimental results on TCP with dierent sizes and Philadelphia benchmark for FAPs show that MAEA-MSFAPs have a good performance and out-perform the compared methods.


12. Shi Cheng, Yuhui Shi, and Quande Qin. Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective. International Journal of Swarm Intelligence Research (IJSIR) 2(3) (2011), 4369. doi: 10.4018/jsir.2011070104.


Abstract: Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get stuck in the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an eective way to moni-tor an algorithms ability of exploration and exploitation. Through the population diversity mea-surement, useful search information can be obtained. PSO with a dierent topology structure and a dierent boundary constraints handling strategy will have a dierent impact on particles exploration and exploitation ability. In this paper, the phenomenon of particles gets stuck in the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with dierent topologies and dierent boundary con-straints handling techniques, and analyze the impact of these setting on the algorithms ability of exploration and exploitation. From these experimental studies, an algorithms ability of explo-ration and exploitation can be observed and the search information obtained; therefore, more eective algorithms can be designed to solve problems.


13. Xin-She Yang. Chaos-Enhanced Firey Algorithm with Automatic Parameter Tuning. Interna-tional Journal of Swarm Intelligence Research (IJSIR) 2(4) (2011), 111. doi: 10 . 4018 / jsir . 2011100101.


Abstract: Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an ecient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, rey algo-rithm was inspired by the ashing behaviour of reies. In this paper, the author extends the standard rey algorithm further to introduce chaos-enhanced rey algorithm with automatic parameter tuning, which results in two more variants of FA. The author rst compares the per-formance of these algorithms, and then uses them to solve a benchmark design problem in engi-neering. Results obtained by other methods will be compared and analyzed.


14. Andreas Janecek and Ying Tan. Swarm Intelligence for Non-Negative Matrix Factorization. In-ternational Journal of Swarm Intelligence Research (IJSIR) 2(4) (2011), 1234. doi: 10.4018/jsir. 2011100102.


Abstract: The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This ar-ticle presents eorts to improve the convergence, approximation quality, and classication ac-curacy of NMF using ve dierent meta-heuristics based on swarm intelligence. Several prop-erties of the NMF objective function motivate the utilization of meta-heuristics: this function is non-convex, discontinuous, and may possess many local minima. The proposed optimization strategies are two-fold: On the one hand, a new initialization strategy for NMF is presented in order to initialize the NMF factors prior to the factorization; on the other hand, an iterative up-date strategy is proposed, which improves the accuracy per runtime for the multiplicative up-date NMF algorithm. The success of the proposed optimization strategies are shown by applying them on synthetic data and data sets coming from the areas of spam ltering/email classica-tion, and evaluate them also in their application context. Experimental results show that both optimization strategies are able to improve NMF in terms of faster convergence, lower approx-imation error, and better classication accuracy. Especially the initialization strategy leads to signicant reductions of the runtime per accuracy ratio for both, the NMF approximation as well as the classication results achieved with NMF.


15. Yuhui Shi. An Optimization Algorithm Based on Brainstorming Process. International Journal of Swarm Intelligence Research (IJSIR) 2(4) (2011), 3562. doi: 10.4018/jsir.2011100103.


Abstract: In this paper, the human brainstorming process is modeled, based on which two ver-sions of Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the eectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in gen-eral. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are dened, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc and/or De, which points at one direction for future research on BSO algorithms.

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Welcome to Swarm Intelligence community

Swarm Intelligence is a field of Computer science. It is a form of Artificial intelligence. Some animals, mostly insects like ants, or bees form large colonies. These colonies are made of many animals that communicate with each other. Each animal is relatively simple, but by working together with other animals it is able to solve complex tasks.

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