Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

Liang, Jing; Qin, A. K.; Suganthan, Ponnuthurai Nagaratnam; Baskar, S.

doi:10.1109/tevc.2005.857610

Cited by 3,327 publications

(1,743 citation statements)

References 33 publications

Supporting

Mentioning

1,721

Contrasting

Unclassified

Order By: Relevance

“…Over the time-modulated linear array design instances we also compare the performance of MOEA/D-DE with that of two single-objective optimization techniques, namely DEGL (DE with Global and Local Neighborhood) [39] and CLPSO (Comprehensive Learning PSO) [40] that are the state-of-the-art variants of DE and PSO, two metaheuristic algorithms widely used in past for various electromagnetic optimization [2,4,26,[41][42][43][44]. For singleobjective optimization techniques, we use a weighted linear sum of the objective functions given in (5a)-(5c).…”

Section: Simulation Resultsmentioning

confidence: 99%

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Pal¹,

Das²,

Basak³

2010

PIER B

View full text Add to dashboard Cite

Abstract-This article proposes a Multi-objective Optimization (MO) framework for the design of time-modulated linear antenna arrays with ultra low maximum Side Lobe Level (SLL), maximum Side Band Level (SBL) and main lobe Beam Width between the First Nulls (BWFN). In contrast to the existing optimization-based methods that attempt to minimize a weighted sum of SLL, SBL, and BWFN, we treat these as three distinct objectives that are to be achieved simultaneously and use one of the best known MultiObjective Evolutionary Algorithms (MOEAs) of current interest called MOEA/D-DE (Decomposition based MOEA with Differential Evolution operator) to determine the best compromise among these three objectives. Unlike the single-objective approaches, the MO approach provides greater flexibility in the design by yielding a set of equivalent final solutions from which the user can choose one that attains a suitable trade-off margin as per requirements. We compared time-modulated antenna structures with other methods for linear array synthesis such as the excitation method and the phase-position synthesis method on the basis of the approximated Pareto Fronts

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Pal¹,

Das²,

Basak³

2010

PIER B

View full text Add to dashboard Cite

show abstract

“…In [10], Peram et al proposed the fitness-distance-ratio based particle swarm optimization (FDR-PSO),by defining the "neighborhood" of a particle as the n closest particles of all particles in the population. Very recently, a comprehensive learning particle swarm optimizer (CLPSO) was proposed to improve the performance of the original PSO on multi-modal problems greatly by a novel learning strategy [12]. Although there are numerous variants of the PSO, they need much time to finish evaluations of fitness function, and give similar results in the early parts of convergence.…”

Section: B Variants Of the Psomentioning

confidence: 99%

“…As indicated in [12], CLPSO's learning strategy abandons the global best information, the past best information of other particles is used to update the particles' velocity instead. In such a way, the CLPSO can significantly improve the performance of the original PSO on multi-modal problems.…”

Section: B Clonal Particle Swarm Optimization (Cpso) Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Clonal particle swarm optimization and its applications

Tan

Xiao

2007

2007 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

Abstract-Particle swarm optimization (PSO) is a stochastic global optimization algorithm inspired by social behavior of bird flocking in search for food, which is a simple but powerful, and widely used as a problem-solving technique to a variety of complex problems in science and engineering. A novel particle swarm optimization algorithm based on immunityclonal strategies, called as clonal particle swarm optimization (CPSO), is proposed at first in this paper. By cloning the best individual of ten succeeding generations, CPSO has better optimization solving capability and faster convergence performance than the conventional standard particle swarm optimization (SPSO) based on a number of simulations. A detailed description and explanation of the CPSO algorithm are given in the paper. Several experiments on six benchmark test functions are conducted to demonstrate that the proposed CPSO algorithm is able to speedup the evolution process and improve the performance of global optimizer greatly, while avoiding the premature convergence on the multidimensional variable space.

show abstract

“…Thus, a reliable optimization algorithm [20][21][22][23] is more suitable for the trajectory planning. Furthermore, both of the overshoot and error should be considered in the optimization object, so a multiobjective optimization based on PSO [24][25][26][27][28][29] (particle swarm optimization) is adopted in this paper.…”

Section: Introductionmentioning

confidence: 99%

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

2018

View full text Add to dashboard Cite

The wafer stage in dual-stage lithographic system is an air-bearing servo motion platform requiring high positioning accuracy and high transient performance. However, the residual vibration, resulting from almost zero damping, high velocity, parallel decoupling structure, and direct drive, brings about unacceptable overshoot and settling time. To suppress these unfavorable elements in high dynamic motion, a novel motion profile planning method combined with input shaping is proposed in this paper. Firstly, a trajectory named all free S-curve (AFS-curve) is derived, which has less constraints and better performance than traditional S-curve profile. Then, AFS-curve combined with a zero vibration shaper (ZV) is developed to further suppress residual vibration. Due to the very complex parameter adjustment, the online tuning may cause system oscillation that leads to damage of the precision stage. This paper, furthermore, proposes an online-offline method to optimize the parameters in the motion profile. Online step is performed to collect input and output data. Offline step includes the system model identification based on I/O data and parameter self-learning based on particle swarm optimization (PSO). The simulation and experimental results indicate that the proposed method achieves significant reduction of the positioning time and the overshoot in the dual-stage system.

show abstract

Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

Cited by 3,327 publications

References 33 publications

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Clonal particle swarm optimization and its applications

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

Contact Info

Product

Resources

About