Particle Swarm Optimization and Intelligence

Parsopoulos, Konstantinos E.; Vrahatis, Michael N.

doi:10.4018/978-1-61520-666-7

Cited by 371 publications

(211 citation statements)

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“…Poli et al [94] stated that the global best model converges faster than the local best model whereas the former model has a higher probability of getting stuck in local optima than the latter model. Surveys of different PSO variations can be found in [95,96].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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“…However, in the research there exist a number of relevant energy preserving techniques, which tends to prolong the network lifespan. In this paper, we have considered one such swarm intelligence mechanism known as Particle Swarm Optimization [4,5].…”

Section: Introductionmentioning

confidence: 99%

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

Singh

Lobiyal

2012

Hum. Cent. Comput. Inf. Sci.

176

103

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The wireless sensor networks have long been an attractive field to the researchers and scientists for its ease in deployment and maintenance. In this research, we focus on the maximization of network lifetime which has become a critical issue in sensor networks. Clustered organization of nodes with aggregation of data at the cluster head becomes one of the significant means to extend life expectancy of the network. This paper proposes Particle Swarm Optimization (PSO) approach for generating energy-aware clusters by optimal selection of cluster heads. The PSO eventually reduces the cost of locating optimal position for the head nodes in a cluster. In addition, we have implemented the PSO-based approach within the cluster rather than base station, which makes it a semi-distributed method. The selection criteria of the objective function are based on the residual energy, intra-cluster distance, node degree and head count of the probable cluster heads. Furthermore, influence of the expected number of packet retransmissions along the estimated path towards the cluster head is also reflected in our proposed energy consumption model. The performance evaluation of our proposed technique is carried out with respect to the well-known cluster-based sensor network protocols, LEACH-C and PSO-C respectively. Finally, the simulation clarifies the effectiveness of our proposed work over its comparatives in terms of network lifetime, average packet transmissions, cluster head selection rounds supported by PSO and average energy consumption.

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“…This distance may depend on its size and desirable behavior. The swarms can also react to the predator by rapidly changing their form, breaking into smaller swarms and re-uniting, illustrating a remarkable ability to respond collectively to external stimuli in order to preserve personal integrity [7,17]. The PSO algorithm consists of a number of particles that collectively move through the search space of the problem in order to find the global optima.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Power System Stability Enhancement using SVC with Modified PSO Tuned PID Controller

HadiAbbas¹,

Humadi²

2013

IJCA

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A modified particle swarm optimization (MPSO) algorithm is utilized in this paper to find out the optimum design of Proportional-Integral-Derivative (PID) controller parameters that improves the stability of a Single-Machine Infinite-Bus (SMIB) system with Static VAR Compensator (SVC). The performance of the power system under different loading conditions is investigated by using three tuning methods. Namely, Ziegler-Nichols-PID, PSO-PID, and MPSO-PID.The simulation results show that the proposed MPSO-PID controlling technique has improved the system response as compared to the two other techniques, that because it gives minimum rise time and minimum settling time with no overshoot and approximately zero steady-state error. All simulations are carried out in MATLAB R2010a software environment.

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Particle Swarm Optimization and Intelligence

Cited by 371 publications

References 0 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks

Power System Stability Enhancement using SVC with Modified PSO Tuned PID Controller

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