scholarly journals Design of Analog Integrated Circuits using Simulated Annealing/Quenching with Crossovers and Particle Swarm Optimization

10.5772/50384 ◽  
2012 ◽  
Author(s):  
Tiago Oliveira ◽  
Wilhelmus A. M. Van Noije
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Integrated Circuits ◽  
Particle Swarm ◽  
Analog Integrated Circuits ◽  
Swarm Optimization

A New Methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing

2011 ◽  
Vol 274 ◽  
pp. 101-111 ◽  
Author(s):  
Norelislam Elhami ◽  
Rachid Ellaia ◽  
Mhamed Itmi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Numerical Models ◽  
Limit State ◽  
Particle Swarm ◽  
Geometrical Parameters ◽  
Rectangular Plates ◽  
Swarm Optimization ◽  
Reliability Methods ◽  
State Functions

This paper presents a new methodology for the Reliability Based Particle Swarm Optimization with Simulated Annealing. The reliability analysis procedure couple traditional and modified first and second order reliability methods, in rectangular plates modelled by an Assumed Modes approach. Both reliability methods are applicable to the implicit limit state functions through numerical models, like those based on the Assumed Mode Method. For traditional reliability approaches, the algorithms FORM and SORM use a Newton-Raphson procedure for estimate design point. In modified approaches, the algorithms are based on heuristic optimization methods such as Particle Swarm Optimization and Simulated Annealing Optimization. Numerical applications in static, dynamic and stability problems are used to illustrate the applicability and effectiveness of proposed methodology. These examples consist in a rectangular plates subjected to in-plane external loads, material and geometrical parameters which are considered as random variables. The results show that the predicted reliability levels are accurate to evaluate simultaneously various implicit limit state functions with respect to static, dynamic and stability criterions.

scholarly journals Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1403 ◽  
Author(s):  
Cheng-Long Wei ◽  
Gai-Ge Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Quantum Behavior

The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems.

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