Energy Loss Assessment for Geothermal Development of High-Temperature Rock Mass in Ocean Plate Based on Particle Swarm Optimization and Interior Point Hybrid Optimization

2019 ◽  
Vol 97 (sp1) ◽  
pp. 170
Author(s):  
Yuwei Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Rock Mass ◽  
High Temperature ◽  
Energy Loss ◽  
Interior Point ◽  
Particle Swarm ◽  
Hybrid Optimization ◽  
Loss Assessment ◽  
Swarm Optimization

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.

Hybrid Optimization Approach for Gas-Assisted Injection Molding Based on Metamodeling and Particle Swarm Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3353-3356
Author(s):  
Wei Chen ◽  
Xian Hong Han ◽  
Xiong Hui Zhou ◽  
Xue Wei Ge
Keyword(s):  
Particle Swarm Optimization ◽  
Injection Molding ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Optimization ◽  
Particle Swarm Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Plastic Process

As a new plastic process technique, Gas-assisted injection molding has many advantages comparing to the traditional injection molding. Meanwhile, Optimization of Gas-assisted injection molding is more complex since many additional parameters have been introduced to the process. In this paper, a hybrid optimization approach based on metamodeling and particle swarm optimization algorithm is proposed and applied for Gas-assisted injection molding. Moreover, the validation of the approach will be illustrated through the optimization process of a real panel.

A hybrid ant colony–particle swarm optimization method (ACOPSO) for aerospace propulsion systems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Altug Piskin ◽  
Tolga Baklacioglu ◽  
Onder Turan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Ant Colony ◽  
Hybrid Optimization ◽  
Difficulty Level ◽  
Propulsion Systems ◽  
Swarm Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Aerospace Propulsion

Purpose The purpose of this paper is to introduce a hybrid, metaheuristic, multimodal and multi-objective optimization tool that is needed for aerospace propulsion engineering problems. Design/methodology/approach Multi-objective hybrid optimization code is integrated with various benchmark and test functions that are selected suitable to the difficulty level of the aero propulsion performance problems. Findings Ant colony and particle swarm optimization (ACOPSO) has performed satisfactorily with benchmark problems. Research limitations/implications ACOPSO is able to solve multi-objective and multimodal problems. Because every optimization problem has specific features, it is necessary to search their general behavior using other algorithms. Practical implications In addition to the optimization solving, ACOPSO enables an alternative methodology for turbine engine performance calculations by using generic components maps. The user is flexible for searching various effects of component designs along with the compressor and turbine maps. Originality/value A hybrid optimization code that has not been used before is introduced. It is targeted use is propulsion systems optimization and design such as Turboshaft or turbofan by preparing the necessary engine functions. A number of input parameters and objective functions can be modified accordingly.

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