• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.295-311
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• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.63-70
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• 2013

Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.

• Structural Engineering and Mechanics
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• v.47 no.4
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• pp.513-530
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• 2013

In this paper, an improved version of particle swarm optimization based optimum design algorithm (IPSO) is presented for the steel grillage systems. The optimum design problem is formulated considering the provisions of American Institute of Steel Construction concerning Load and Resistance Factor Design. The optimum design algorithm selects the appropriate W-sections for the beams of the grillage system such that the design constraints are satisfied and the grillage weight is the minimum. When an improved version of the technique is extended to be implemented, the related results and convergence performance prove to be better than the simple particle swarm optimization algorithm and some other metaheuristic optimization techniques. The efficiency of different inertia weight parameters of the proposed algorithm is also numerically investigated considering a number of numerical grillage system examples.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.176-179
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• 2007

In this paper, we develop the path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1].

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.423-430
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• 2010

Particle swarm optimization (PSO) algorithm is designed to find a single global optimal point. However, the PSO needs to be modified in order to find multiple optimal points of a multimodal function. These modifications usually divide a swarm of particles into multiple subswarms; in turn, these subswarms try to find their own optimal point, resulting in multiple optimal points. In this work, we present a new PSO algorithm, called coupling PSO to find multiple optimal points of a multimodal function based on coupling particles. In the coupling PSO, each main particle may generate a new particle to form a couple, after which the couple searches its own optimal point using non-stop-moving PSO algorithm. We tested the suggested algorithm and other ones, such as clustering PSO and niche PSO, over three analytic functions. The coupling PSO algorithm was also applied to solve a significant benchmark problem, the TEAM workshop problem 22.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.9
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• pp.55-61
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• 2009

This paper presents a modified Phasor Discrete Particle Swarm Optimization (PDPSO) algorithm to configure Community Energy Systems(CESs) in the distribution system. The CES obtains electric power from its own Distributed Generations(DGs) and purchases insufficient power from the competitive power market, to supply power for customers contracted with the CES. When there are two or more CESs in a network, the CESs will continue the competitive expansion to reduce the total operation cost. The particles of the proposed PDPSO algorithm have magnitude and phase angle values, and move within a circle area. In the case study, the results by PDPSO algorithm was compared with that by the conventional DPSO algorithm.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1027-1037
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• 2017

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4987-5005
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• 2017

This paper introduces a new method to solve Similarity Recognition for Cloth Products, which is based on Fuzzy logic and Particle swarm optimization algorithm. For convenience, it is called the SRCPFP method hereafter. In this paper, the SRCPFP method combines Fuzzy Logic (FL) and Particle Swarm Optimization (PSO) algorithm to solve similarity recognition for cloth products. First, it establishes three features, length, thickness, and temperature resistance, respectively, for each cloth product. Subsequently, these three features are engaged to construct a Fuzzy Inference System (FIS) which can find out the similarity between a query cloth and each sampling cloth in the cloth database D. At the same time, the FIS integrated with the PSO algorithm can effectively search for near optimal parameters of membership functions in eight fuzzy rules of the FIS for the above similarities. Finally, experimental results represent that the SRCPFP method can realize a satisfying recognition performance and outperform other well-known methods for similarity recognition under considerations here.

• Journal of IKEEE
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• v.22 no.2
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• pp.455-459
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• 2018

In this paper, proposed a multi-channel charging control strategy for electric vehicle. This control strategy can adjust the charging power according to the calculated state-of-charge (SOC). Electric vehicle (EV) charging system using Particle Swarm Optimization (PSO) algorithm is proposed. A stochastic optimization algorithm technique such as PSO in the time-of-use (TOU) price used for the energy cost minimization. Simulation results show that the energy cost can be reduced using proposed method.