Volume 1, 2010

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List of IJSIR published papers

Shi Cheng, Junfeng Chen, and Yuhui Shi June 2017

Volume 1, 2010

1. Kevin M. Passino. Bacterial Foraging optimization. International Journal of Swarm Intelligence

Research (IJSIR) 1(1) (2010), 116. doi: 10.4018/jsir.2010010101.

Abstract: The bacterial foraging optimization (BFO) algorithm mimics how bacteria forage over a landscape of nutrients to perform parallel nongradient optimization. In this article, the author provides a tutorial on BFO, including an overview of the biology of bacterial foraging and the pseudo-code that models this process. The algorithms features are briey compared to those in genetic algorithms, other bio-inspired methods, and nongradient optimization. The applications and future directions of BFO are also presented.

2. Robert G. Reynolds and Leonard Kinnaird-Heether. Networks Do Matter: The Socially Moti-vated design of a 3D race Controller using Cultural algorithms. International Journal of Swarm Intelligence Research (IJSIR) 1(1) (2010), 1741. doi: 10.4018/jsir.2010010102.

Abstract: This article describes a socially motivated evolutionary algorithm, Cultural Algo-rithms, to design a controller for a 3D racing game for use in a competitive event held at the 2008 IEEE World Congress. The controller was modeled as a state machine and a set of utility functions were associated with actions performed in each state. Cultural Algorithms are used to optimize these functions. Cultural Algorithms consist of a Population Space, a collection of knowledge sources in the Belief Space, and a communication protocol connecting the compo-nents together. The knowledge sources in the belief space vie to control individuals in the pop-ulation through the social fabric inuence function. Here the population is a network of chro-mosomes connected by the LBest topology. This LBest conguration was employed to train the system on an example oval track prior to the contest, but it did not generalize to other tracks. The authors investigated how other topologies performed when learning on each of the con-test tracks. The square network (a type of small world network) worked best at distributing the inuence of the knowledge sources, and reduced the likelihood of premature convergence for complex tracks.

3. Wen Fung Leong and Gary G. Yen. Constraint Handling in Particle Swarm optimization. Inter-national Journal of Swarm Intelligence Research (IJSIR) 1(1) (2010), 4263. doi: 10 . 4018 / jsir . 2010010103.

Abstract: In this article, the authors propose a particle swarm optimization (PSO) for con-strained optimization. The proposed PSO adopts a multiobjective approach to constraint han-dling. Procedures to update the feasible and infeasible personal best are designed to encourage nding feasible regions and convergence toward the Pareto front. In addition, the infeasible non-dominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO ight equations. The purpose is to nd more feasible particles and search for better solutions during the process. The mutation proce-dure is applied to encourage global and ne-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

4. Ying Tan. Particle Swarm optimization algorithms Inspired by Immunity-Clonal Mechanism and Their applications to Spam detection. International Journal of Swarm Intelligence Research (IJSIR) 1(1) (2010), 6486. doi: 10.4018/jsir.2010010104.

Abstract: Compared to conventional PSO algorithm, particle swarm optimization algorithms in-spired by immunity-clonal strategies are presented for their rapid convergence, easy implemen-tation and ability of optimization. A novel PSO algorithm, clonal particle swarm optimization (CPSO) algorithm, is proposed based on clonal principle in natural immune system. By cloning the best individual of successive generations, the CPSO enlarges the area near the promising candidate solution and accelerates the evolution of the swarm, leading to better optimization capability and faster convergence performance than conventional PSO. As a variant, an advance-and-retreat strategy is incorporated to nd the nearby minima in an enlarged solution space for greatly accelerating the CPSO before the next clonal operation. A black hole model is also es-tablished for easy implementation and good performance. Detailed descriptions of the CPSO algorithm and its variants are elaborated. Extensive experiments on 15 benchmark test functions demonstrate that the proposed CPSO algorithms speedup the evolution procedure and improve the global optimization performance. Finally, an application of the proposed PSO algorithms to spam detection is provided in comparison with the other three methods.

5. Xiangyin Zhang, Haibin Duan, Shan Shao, and Yunhui Wang. Design of Multi-Criteria PI Con-troller Using Particle Swarm Optimization for Multiple UAVs Close Formation. International Journal of Swarm Intelligence Research (IJSIR) 1(2) (2010), 117. doi: 10.4018/jsir.2010040101.

Abstract: Close formation ight is one of the most complicated problems on multiple Uninhab-ited Aerial Vehicles (UAVs) coordinated control. This paper proposes a new method to achieve close formation tracking control of multiple UAVs by applying Particle Swarm Optimization (PSO) based Proportional plus Integral (PI) controller. Due to its simple structure and eective-ness, multi-criteria PI control strategy is employed to design the controller for multiple UAVs formation, while PSO is used to optimize the controller parameters on-line. With the inclusion of overshoot, rise time, and system accumulated absolute error in the multi-criteria performance index, the overall performance of multi-criteria PI controller is optimized to be satisfactory. Sim-ulation results show the feasibility and eectiveness of the proposed approach.

6. T. O. Ting, H. C. Ting, and T. S. Lee. Taguchi-Particle Swarm Optimization for Numerical Op-timization. International Journal of Swarm Intelligence Research (IJSIR) 1(2) (2010), 1833. doi: 10.4018/jsir.2010040102.

Abstract: In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give signicant impact in terms of computational cost. The method creates a more diversied population, which also contributes to the success of avoiding premature convergence. The proposed method is ef-fectively applied to solve 13 benchmark problems. This studys results show drastic improve-ments in comparison with the standard PSO algorithm involving continuous and discrete vari-ables on high dimensional benchmark functions.

7. William M. Spears, Derek T. Green, and Diana F. Spears. Biases in Particle Swarm Optimization. International Journal of Swarm Intelligence Research (IJSIR) 1(2) (2010), 3457. doi: 10 .4018 / jsir.2010040103.

Abstract: The most common versions of particle swarm optimization (PSO) algorithms are ro-tationally variant. It has also been pointed out that PSO algorithms can concentrate particles along paths parallel to the coordinate axes. In this paper, the authors explicitly connect these two observations by showing that the rotational variance is related to the concentration along lines parallel to the coordinate axes. Based on this explicit connection, the authors create tness functions that are easy or hard for PSO to solve, depending on the rotation of the function.

8. Yan Meng and Yaochu Jin. Distributed Multi-Agent Systems for a Collective Construction Task based on Virtual Swarm Intelligence. International Journal of Swarm Intelligence Research (IJSIR) 1(2) (2010), 5879. doi: 10.4018/jsir.2010040104.

Abstract: In this paper, a virtual swarm intelligence (VSI)-based algorithm is proposed to co-ordinate a distributed multi-robot system for a collective construction task. Three phases are involved in a construction task: search, detect, and carry. Initially, robots are randomly located within a bounded area and start random search for building blocks. Once the building blocks are detected, agents need to share the information with their local neighbors. A distributed vir-tual pheromone-trail (DVP) based model is proposed for local communication among agents. If multiple building blocks are detected in a local area, agents need to make decisions on which agent(s) should carry which block(s). To this end, a virtual particle swarm optimization (V-PSO)-based model is developed for multi-agent behavior coordination. Furthermore, a quorum sens-ing (QS)-based model is employed to balance the tradeo between exploitation and exploration, so that an optimal overall performance can be achieved. Extensive simulation results on a col-lective construction task have demonstrated the eciency and robustness of the proposed VSI-based framework.

9. Kevin M. Passino. Honey Bee Swarm Cognition: Decision-Making Performance and Adaptation. International Journal of Swarm Intelligence Research (IJSIR) 1(2) (2010), 8097. doi: 10 .4018 / jsir.2010040105.

Abstract: A synthesis of ndings from neuroscience, psychology, and behavioral biology has been recently used to show that several key features of cognition in neuron-based brains of ver-tebrates are also present in bee-based swarms of honey bees. Here, simulation tests are adminis-tered to the honey bee swarm cognition system to study its decision-making performance. First, tests are used to evaluate the ability of the swarm to discriminate between choice options and avoid picking inferior distractor options. Second, a Treisman feature search test from psychol-ogy, and tests of irrationality developed for humans, are administered to show that the swarm possesses some features of human decision-making performance. Evolutionary adaptation of swarm decision making is studied by administering swarm choice tests when there are varia-tions on the parameters of the swarms decision-making mechanisms. The key result is that in addition to trading o decision-making speed and accuracy, natural selection seems to have set-tled on parameters that result in individual bee-level assessment noise being eectively ltered out to not adversely aect swarm-level decision-making performance.

10. M. A. Abido and Saleh M. Bamasak. Oscillation Damping Enhancement via Coordinated Design of PSS and FACTS-Based Stabilizers in a Multi-Machine Power System Using PSO. International Journal of Swarm Intelligence Research (IJSIR) 1(3) (2010), 118. doi: 10.4018/jsir.2010070101.

Abstract: This paper investigates the enhancement of power system stability via coordinated design of Power System Stabilizers (PSSs), Thyristor Controlled Series Capacitor (TCSC)-based stabilizer, and Static Var Compensator (SVC)-based stabilizer in a multi-machine power system. The design problem of the proposed stabilizers is formulated as an optimization problem. Us-ing the developed linearized power system model, the particle swarm optimization (PSO) algo-rithm is employed to search for optimal stabilizer settings that maximize the minimum damp-ing ratio of all system oscillating modes. The proposed stabilizers are evaluated on a two-area weakly-connected multi-machine power system with unstable interarea oscillation mode. The nonlinear simulation results and eigenvalue analysis show the eectiveness of the proposed co-ordinated stabilizers in damping low frequency power system oscillations and enhancing the system stability.

11. Anil Kumar Ramakuru, Siva G. Kumar, Kalyan B. Kumar, and Mahesh K. Mishra. Compensation of Voltage Sags with Phase-Jumps through DVR with Minimum VA Rating Using PSO based AN-FIS Controller. International Journal of Swarm Intelligence Research (IJSIR) 1(3) (2010), 1933. doi:


Abstract: Dynamic Voltage Restorer (DVR) restores the distribution system load voltage to a nominal balanced sinusoidal voltage, when the source voltage has distortions, sag/swell and un-balances. DVR has to inject a required amount of Volt-Amperes (VA) into the system to maintain a nominal balanced sinusoidal voltage at the load. Keeping the cost eectiveness of DVR, it is desirable to have a minimum VA rating of the DVR, for a given system without compromising compensation capability. In this regard, a methodology has been proposed in this work to min-imize VA rating of DVR. The optimal angle at which DVR voltage has to be injected in series to the line impedance so as to have minimum VA loading on DVR as well as the removal of phase jumps in the three-phases is computed by the Particle Swarm Optimization (PSO) technique. The proposed method is able to compensate voltage sags with phase jumps by keeping the DVR voltage and power ratings minimum, eectively. The proposed PSO methodology together with adaptive neuroCfuzzy inference system used to make the DVR work online with minimum VA loading. The proposed method has been validated through detailed simulation studies.

12. P. K. Roy, S. P. Ghoshal, and S. S. Thakur. Optimal Power Flow with TCSC and TCPS Model-ing using Craziness and Turbulent Crazy Particle Swarm Optimization. International Journal of Swarm Intelligence Research (IJSIR) 1(3) (2010), 3450. doi: 10.4018/jsir.2010070103.

Abstract: This paper presents two new Particle swarm optimization methods to solve optimal power ow (OPF) in power system incorporating exible AC transmission systems (FACTS). Two types of FACTS devices, thyristor-controlled series capacitor (TCSC) and thyristor controlled phase shifting (TCPS), are considered. In this paper, the problems of OPF with FACTS are solved by using particle swarm optimization with the inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), craziness based particle swarm optimization (CRPSO), and turbulent crazy particle swarm optimization (TRPSO). The proposed methods are implemented on mod-ied IEEE 30-bus system for four dierent cases. The simulation results show better solution quality and computation eciency of TRPSO and CRPSO algorithms over PSOIWA and RGA. The study also shows that FACTS devices are capable of providing an economically attractive solution to OPF problems.

13. Sujatha Balaraman and N. Kamaraj. Congestion Management Using Hybrid Particle Swarm Op-timization Technique. International Journal of Swarm Intelligence Research (IJSIR) 1(3) (2010), 51 66. doi: 10.4018/jsir.2010070104.

Abstract: This paper proposes the Hybrid Particle Swarm Optimization (HPSO) method for solv-ing congestion management problems in a pool based electricity market. Congestion may occur due to lack of coordination between generation and transmission utilities or as a result of un-expected contingencies. In the proposed method, the control strategies to limit line loading to the security limits are by means of minimum adjustments in generations from the initial market clearing values. Embedding Evolutionary Programming (EP) technique in Particle Swarm Op-timization (PSO) algorithm improves the global searching capability of PSO and also prevents the premature convergence in local minima. A number of functional operating constraints, such as branch ow limits and load bus voltage magnitude limits are included as penalties in the t-ness function. Numerical results on three test systems namely modied IEEE 14 Bus, IEEE 30 Bus and IEEE 118 Bus systems are presented and the results are compared with PSO and EP ap-proaches in order to demonstrate its performance.

14. K. Vaisakh and L. R. Srinivas. Unit Commitment by Evolving Ant Colony Optimization. Inter-national Journal of Swarm Intelligence Research (IJSIR) 1(3) (2010), 6777. doi: 10 . 4018 / jsir . 2010070105.

Abstract: Ant Colony Optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem, and multistage decision mak-ing of ACO has an edge over other conventional methods. In this paper, the authors propose the Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) prob-lem. The EACO employs Genetic Algorithm (GA) for nding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, start up cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on the systems with number of gen-erating units in the range of 10 to 60. The test results are encouraging and compared with those obtained by other methods.

15. Gomaa Zaki El-Far. Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Opti-mization. International Journal of Swarm Intelligence Research (IJSIR) 1(4) (2010), 116. doi: 10 . 4018/jsir.2010100101.

Abstract: This paper proposes a modied particle swarm optimization algorithm (MPSO) to de-sign adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dy-namical systems. The modication of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algo-rithm uses a minimum velocity threshold to control the velocity of the particles, avoids cluster-ing of the particles, and maintains the diversity of the population in the search space. The mech-anism of MPSO has better potential to explore good solutions in new search spaces. The pro-posed MPSO algorithm is also used to tune and optimize the controller parameters like the scal-ing factors, the membership functions, and the rule base. To illustrate the adaptation process, the proposed neuro-fuzzy controller based on MPSO algorithm is applied successfully to con-trol the behavior of both non-linear single machine power systems and non-linear inverted pen-dulum systems. Simulation results demonstrate that the adaptive neuro-fuzzy logic controller application based on MPSO can eectively and robustly enhance the damping of oscillations.

16. Antons Rebguns, Diana F. Spears, Richard Anderson-Sprecher, and Aleksey Kletsov. A Theoret-ical Framework for Estimating Swarm Success Probability Using Scouts. International Journal of Swarm Intelligence Research (IJSIR) 1(4) (2010), 1745. doi: 10.4018/jsir.2010100102.

Abstract: This paper presents a novel theoretical framework for swarms of agents. Before de-ploying a swarm for a task, it is advantageous to predict whether a desired percentage of the swarm will succeed. The authors present a framework that uses a small group of expendable scout agents to predict the success probability of the entire swarm, thereby preventing many agent losses. The scouts apply one of two formulas to predict C the standard Bernoulli trials for-mula or the new Bayesian formula. For experimental evaluation, the framework is applied to simulated agents navigating around obstacles to reach a goal location. Extensive experimental results compare the mean-squared error of the predictions of both formulas with ground truth, under varying circumstances. Results indicate the accuracy and robustness of the Bayesian ap-proach. The framework also yields an intriguing result, namely, that both formulas usually pre-dict better in the presence of (Lennard-Jones) inter-agent forces than when their independence assumptions hold.

17. Maurice Clerc. Beyond Standard Particle Swarm Optimisation. International Journal of Swarm Intelligence Research (IJSIR) 1(4) (2010), 4661. doi: 10.4018/jsir.2010100103.

Abstract: Currently, two very similar versions of PSO are available that could be called standard. While it is easy to merge them, their common drawbacks still remain. Therefore, in this paper, the author goes beyond simple merging by suggesting simple yet robust changes and solving a few well-known, common problems, while retaining the classical structure. The results can be proposed to the swarmer community as a new standard.

2018-09-19 11:11

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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