Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm

Sun, Haoyu

doi:10.1016/j.engappai.2009.09.005

Cited by 75 publications

(25 citation statements)

References 10 publications

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“…The results show that this technique can find the optimal value of two objectives which increasing the efficiency of task scheduling on CMP and decreasing the execution time and energy consumption of the system. Sun Hui (2010) proposed Adaptive Simulated Annealing Genetic Algorithm (ASAGA) to find design parameters for maximum fuel economy [27]. Different objectives were investigated to find the optimal results, such as performance, energy regenerative ability, fuel economy, etc.…”

Section: Discussion About Future Workmentioning

confidence: 99%

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Optimization Of Thermo-Electric Coolers Using Hybrid Genetic Algorithm And Simulated Annealing

Khanh¹,

Vasant²,

Elamvazuthi³

et al. 2014

Archives of Control Sciences

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Thermo-electric Coolers (TECs) nowadays are applied in a wide range of thermal energy systems. This is due to their superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environmentally friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this paper, the review of some previous researches will be conducted to see the diversity of optimization in the design of TECs in enhancing the performance and efficiency. After that, single-objective optimization problems (SOP) will be tested first by using Genetic Algorithm (GA) and Simulated Annealing (SA) to optimize geometry properties so that TECs will operate at near optimal conditions. Equality constraint and inequality constraint were taken into consideration.

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Section: Discussion About Future Workmentioning

confidence: 99%

“…The result obtained using GA were disappointing, especially in the larger problems [16]. SA does not converge as fast as the GA in the initial phase [27].…”

Section: Meta-heuristic Optimization Algorithmmentioning

confidence: 99%

Optimization Of Thermo-Electric Coolers Using Hybrid Genetic Algorithm And Simulated Annealing

Khanh¹,

Vasant²,

Elamvazuthi³

et al. 2014

Archives of Control Sciences

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“…Derivative-free methods, such as genetic algorithms [6][7][8][9][10][11] or particle swarm optimization [12][13][14] have been proven to be a suitable approach to solve the HEV design optimization problem. However, most of these methods convert the multi-objective optimization problem into a single objective optimization problem by allocating weights to each of the objective functions (a priori methods).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

Fang

Qin

et al. 2011

Energies

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Compared to conventional vehicles Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions. To enhance HEV performance in terms of fuel economy and emissions, and ensure user satisfaction with driving performance, the need for simultaneous optimization for the main parameters of powertrain components and control system is inevitable. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Considering the defect of the methods which convert multi-objective optimization problems into single-objective ones, a comprehensive methodology based on the non-dominated sorting genetic algorithms II (NSGA II) to achieve parameter optimization for powertrain components and control system simultaneously and successfully find the Pareto-optimal solutions set is presented in this paper. A case simulation is carried out and simulated by ADVISOR, The simulation results show that this method can produce many Pareto-optimal solutions and a satisfactory solution can be selected by decision-makers according to their requirements. The results demonstrate the effectiveness of the algorithms proposed in this paper.

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“…Many evolutionary algorithms such as the genetic algorithm (GA) [5], particle swarm optimization (PSO) [6], simulated annealing (SA) [7], tabu search (TS) [8], ant colony optimization (ACO) [9], and differential evolution (DE) [10], were proposed to solve the practical economic dispatch problem. In [1]- [11], authors present the major contributions of this second category in power system operation and control.…”

Section: Introductionmentioning

confidence: 99%

Solving Practical Economic Dispatch Problems Using Improved Artificial Bee Colony Method

Mahdad¹,

Srairi²

2014

IJISA

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Abstract-This paper presents an improved artificial bee colony (IABC) optimization method to solving practical economic dispatch taking into account the nonlinear generator characteristics such as valve-point loading effects. In order to exploit the performance of this new variant based ABC method to solving practical economic dispatch, a new local search mechanism (LSM) associated to the original ABC algorithm; it allows exploiting effectively the promising region to locate the best solution. The proposed approach has been examined and applied to many practical electrical power systems, the 13 generating units, and to the large electrical system with 40 generating units considering valve point loading effects. From the different case studies, it is observed that the results compared with the other recent techniques demonstrate the potential of the proposed approach and show clearly its effectiveness to solve practical and large ED.

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Multi-objective optimization for hydraulic hybrid vehicle based on adaptive simulated annealing genetic algorithm

Cited by 75 publications

References 10 publications

Optimization Of Thermo-Electric Coolers Using Hybrid Genetic Algorithm And Simulated Annealing

Optimization Of Thermo-Electric Coolers Using Hybrid Genetic Algorithm And Simulated Annealing

Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms

Solving Practical Economic Dispatch Problems Using Improved Artificial Bee Colony Method

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