Solving nonlinear optimal control problems using a hybrid IPSO–SQP algorithm
نویسنده:
, , ,سال
: 2011
چکیده: A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO–SQP, is proposed for solving nonlinear optimal control problems. The particle swarm optimization (PSO) is showed to converge rapidly to a near optimum solution, but the search process will become very slow around global optimum. On the contrary, the ability of SQP is weak to escape local optimum but can achieve faster convergent speed around global optimum and the convergent accuracy can be higher. Hence, in the proposed method, at the beginning stage of search process, a PSO algorithm is employed to find a near optimum solution. In this case, an improved PSO (IPSO) algorithm is used to enhance global search ability and convergence speed of algorithm. When the change in fitness value is smaller than a predefined value, the searching process is switched to SQP to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To validate the performance of the proposed IPSO–SQP approach, it is evaluated on two optimal control problems. Results show that the performance of the proposed algorithm is satisfactory.
کلیدواژه(گان): Optimal control,Optimization,Particle swarm optimization,Inertia weight,Successive quadratic programming
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Solving nonlinear optimal control problems using a hybrid IPSO–SQP algorithm
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contributor author | حمید رضا مدرّس | en |
contributor author | محمدباقر نقیبی سیستانی | en |
contributor author | Hamidreza Modares | fa |
contributor author | Mohammad Bagher Naghibi Sistani | fa |
date accessioned | 2020-06-06T14:34:26Z | |
date available | 2020-06-06T14:34:26Z | |
date issued | 2011 | |
identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3402419 | |
description abstract | A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO–SQP, is proposed for solving nonlinear optimal control problems. The particle swarm optimization (PSO) is showed to converge rapidly to a near optimum solution, but the search process will become very slow around global optimum. On the contrary, the ability of SQP is weak to escape local optimum but can achieve faster convergent speed around global optimum and the convergent accuracy can be higher. Hence, in the proposed method, at the beginning stage of search process, a PSO algorithm is employed to find a near optimum solution. In this case, an improved PSO (IPSO) algorithm is used to enhance global search ability and convergence speed of algorithm. When the change in fitness value is smaller than a predefined value, the searching process is switched to SQP to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To validate the performance of the proposed IPSO–SQP approach, it is evaluated on two optimal control problems. Results show that the performance of the proposed algorithm is satisfactory. | en |
language | English | |
title | Solving nonlinear optimal control problems using a hybrid IPSO–SQP algorithm | en |
type | Journal Paper | |
contenttype | External Fulltext | |
subject keywords | Optimal control | en |
subject keywords | Optimization | en |
subject keywords | Particle swarm optimization | en |
subject keywords | Inertia weight | en |
subject keywords | Successive quadratic programming | en |
journal title | Engineering Applications of Artificial Intelligence | fa |
pages | 476-484 | |
journal volume | 24 | |
journal issue | 3 | |
identifier link | https://profdoc.um.ac.ir/paper-abstract-1020299.html | |
identifier articleid | 1020299 |