scholarly journals Particle swarm optimization on trade-off extraction of analog integrated circuits

2009 ◽  
Vol 6 (23) ◽  
pp. 1643-1648
Author(s):  
Ali Beirami ◽  
Mohammad Takhti
Keyword(s):  
Particle Swarm Optimization ◽  
Integrated Circuits ◽  
Particle Swarm ◽  
Analog Integrated Circuits ◽  
Swarm Optimization ◽  
Trade Off

scholarly journals Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Qian Zhang ◽  
Jinjin Ding ◽  
Weixiang Shen ◽  
Jinhui Ma ◽  
Guoli Li
Keyword(s):  
Particle Swarm Optimization ◽  
Greenhouse Gas Emission ◽  
Particle Swarm ◽  
Pareto Optimization ◽  
Environmental Cost ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Trade Off ◽  
Multiobjective Particle Swarm Optimization ◽  
Operation Cost

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

Stochastic Weight Trade-Off Particle Swarm Optimization for Optimal Power Flow

2014 ◽  
Vol 2 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Luong Dinh Le ◽  
Jirawadee Polprasert ◽  
Weerakorn Ongsakul ◽  
Dieu Ngoc Vo ◽  
Dung Anh Le
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Trade Off ◽  
Optimal Power

Synthesis of Analog Circuits by Genetic Algorithms and their Optimization by Particle Swarm Optimization

2010 ◽  
pp. 173-192 ◽  
Author(s):  
Esteban Tlelo-Cuautle ◽  
Ivick Guerra-Gómez ◽  
Carlos Alberto Reyes-García ◽  
Miguel Aurelio Duarte-Villaseñor
Keyword(s):  
Particle Swarm Optimization ◽  
Integrated Circuits ◽  
Analog Circuits ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Cmos Technology ◽  
Nsga Ii ◽  
Open Problems ◽  
Swarm Optimization ◽  
Circuit Simulator

This chapter shows the application of particle swarm optimization (PSO) to size analog circuits which are synthesized by a genetic algorithm (GA) from nullor-based descriptions. First, a historical description of the development of automatic synthesis techniques to design analog circuits is presented. Then, the synthesis of analog circuits by applying a GA at the transistor level of abstraction is demonstrated. After that, the authors present the proposed multi-objective (MO) PSO algorithm which makes calls to the circuit simulator HSPICE to evaluate performances until optimal sizes of the transistors are found by using standard CMOS technology of 0.35µm of integrated circuits. Finally, the MO-PSO algorithm is compared with NSGA-II, and some open problems oriented to circuit synthesis and sizing are briefly discussed.

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