Data processing with an Improved Hybrid Optimization Algorithm Base on PSO-GA

2014 ◽  
Vol 1014 ◽  
pp. 404-412 ◽  
Author(s):  
Fu Kun Zhang ◽  
Shu Wen Zhang ◽  
Gui Zhi Ba
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Hybrid Optimization ◽  
Swarm Optimization ◽  
Algorithm Performance ◽  
Hybrid Optimization Algorithm ◽  
Exchange Information ◽  
Algorithm Base ◽  
The Impact ◽  
Crossover And Mutation

This paper develops an improved hybrid optimization algorithm based on particle swarm optimization (PSO) and a genetic algorithm (GA). First, the population is evolved over a certain number of generations by PSO and the best M particles are retained, with the remaining particles excluded. Second, new individuals are generated by implementing selection, crossover and mutation GA operators for the best M particles. Finally, the new individuals are combined with the best M particles to form new a population for the next generation. The algorithm can exchange information several times during evolution so that the complement of two algorithms can be more fully exploited. The proposed method is applied to fifteen benchmark optimization problems and the results obtained show an improvement over published methods. The impact of M on algorithm performance is also discussed.

A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

2017 ◽  
pp. 491-521 ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
Aboul Ella Hassanien
Keyword(s):  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

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.

scholarly journals A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Shuang Wang ◽  
Qingxin Liu ◽  
Yuxiang Liu ◽  
Heming Jia ◽  
Laith Abualigah ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Salp Swarm Algorithm ◽  
Constrained Problems ◽  
Opposition Based Learning ◽  
Hybrid Optimization Algorithm ◽  
Engineering Problems ◽  
Swarm Algorithm

Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms’ advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition-based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique’s practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real-world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation.

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