A Condensed Hybrid Optimization Algorithm Using Enhanced Continuous Tabu Search and Particle Swarm Optimization

2009 ◽  
Author(s):  
Cheng-Hung Chen ◽  
Marco P. Schoen ◽  
Ken W. Bosworth
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Particle Swarm ◽  
Search Space ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Hybrid Optimization Algorithm ◽  
Promising Area

A novel Condensed Hybrid Optimization (CHO) algorithm using Enhanced Continuous Tabu Search (ECTS) and Particle Swarm Optimization (PSO) is proposed. The proposed CHO algorithm combines the respective strengths of ECTS and PSO. The ECTS is a modified Tabu Search (TS), which has good search capabilities on large search spaces. In this study, ECTS is utilized to define smaller search spaces, which are used in a second stage by the basic PSO to find the respective local optimum. The ECTS covers the global search space by using a TS concept called diversification and then selects the most promising areas in the search space. Once the promising regions in the search space are defined, the proposed CHO algorithm employs another TS concept called intensification in order to search the promising area thoroughly. The proposed CHO algorithm is tested with the multi-dimensional Hyperbolic and Rosenbrock problems. Compared to other four algorithms, the simulations results indicate that the accuracy and effectiveness of the proposed CHO algorithm.

Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

2013 ◽  
pp. 31-42
Author(s):  
Yuhong Chi ◽  
Fuchun Sun ◽  
Langfan Jiang ◽  
Chunyang Yu ◽  
Chunli Chen
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quotient Space ◽  
Particle Swarm ◽  
Search Space ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Speed

To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle swarm optimization algorithm, this paper applies the theory of topology, and proposed a quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism. In QsaBC, Search space-zoomed factor and Attractor are introduced according to the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions are selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can achieve the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective with errant particles, and has easier calculation and better robustness than other methods.

Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Optimization

2012 ◽  
pp. 81-98
Author(s):  
Gomaa Zaki El-Far
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Control Approach ◽  
Search Spaces ◽  
Neuro Fuzzy ◽  
Non Linear

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold to control the velocity of the particles, avoids clustering of the particles, and maintains the diversity of the population in the search space. The mechanism of MPSO has better potential to explore good solutions in new search spaces. The proposed MPSO algorithm is also used to tune and optimize the controller parameters like the scaling factors, the membership functions, and the rule base. To illustrate the adaptation process, the proposed neuro-fuzzy controller based on MPSO algorithm is applied successfully to control the behavior of both non-linear single machine power systems and non-linear inverted pendulum systems. Simulation results demonstrate that the adaptive neuro-fuzzy logic controller application based on MPSO can effectively and robustly enhance the damping of oscillations.

Quotient Space-Based Boundary Condition for Particle Swarm Optimization Algorithm

2011 ◽  
Vol 3 (1) ◽  
pp. 78-89
Author(s):  
Yuhong Chi ◽  
Fuchun Sun ◽  
Langfan Jiang ◽  
Chunyang Yu ◽  
Chunli Chen
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quotient Space ◽  
Particle Swarm ◽  
Search Space ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Speed

To control particles to fly inside the limited search space and deal with the problems of slow search speed and premature convergence of particle swarm optimization algorithm, this paper applies the theory of topology, and proposed a quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism. In QsaBC, Search space-zoomed factor and Attractor are introduced according to the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC’2008 benchmark functions are selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can achieve the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective with errant particles, and has easier calculation and better robustness than other methods.

Hybrid Optimization Algorithm Using Enhanced Continuous Tabu Search and Genetic Algorithm for Parameter Estimation

2006 ◽  
Author(s):  
Barathram Ramkumar ◽  
Marco P. Schoen ◽  
Feng Lin ◽  
Brian G. Williams
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Tabu Search ◽  
Search Space ◽  
The Novel ◽  
Search Spaces ◽  
Estimation Algorithms ◽  
Hybrid Optimization Algorithm ◽  
Estimation Problems ◽  
Promising Area

A new algorithm using Enhanced Continuous Tabu Search (ECTS) and genetic algorithm (GA) is proposed for parameter estimation problems. The proposed algorithm combines the respective strengths of ECTS and GA. The ECTS is a modified Tabu Search (TS), which has good search capabilities for large search spaces. In this work, the ECTS is used to define smaller search spaces, which are used in a second stage by a GA to find the respective local minima. The ECTS covers the global search space by using a TS concept called diversification and then selects the most promising regions in the search space. Once the promising areas in the search space are identified, the proposed algorithm employs another TS concept called intensification in order to search the promising area thoroughly. The proposed algorithm is tested with benchmark multimodal functions for which the global minimum is known. In addition, the novel algorithm is used for parameter estimation problems, where standard estimation algorithms encounter problems estimating the parameters in an un-biased fashion. The simulation results indicate the effectiveness of the proposed hybrid algorithm.

A Novel Hybrid Optimization Method of Shuffled Frog Leaping Algorithm and Particle Swarm Optimization

2013 ◽  
Vol 717 ◽  
pp. 433-438 ◽  
Author(s):  
Mei Jin Lin ◽  
Fei Luo ◽  
Yu Ge Xu ◽  
Long Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Search Strategy ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

scholarly journals Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jianwen Guo ◽  
Zhenzhong Sun ◽  
Hong Tang ◽  
Xuejun Jia ◽  
Song Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Preventive Maintenance ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Hybrid Optimization ◽  
Normal Operation ◽  
Maintenance Period ◽  
Swarm Optimization ◽  
Hybrid Optimization Algorithm

All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

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