Papers by Keyword: Multi-Objective

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Authors: Bashra Kadhim Oleiwi, Hubert Roth, Bahaa I. Kazem
Abstract: In this study, we developed an Ant Colony Optimization (ACO) - Genetic Algorithm (GA) hybrid approach for solving the Multi objectives Optimization global path planning (MOPP) problem of mobile robot. The ACO optimization algorithm is used to find the sub-optimal collision free path which then used as initial population for GA. In the proposed modified genetic algorithms, specific genetic operator such as deletion operator is proposed, which is based on domain heuristic knowledge, to fit the optimum path planning for mobile robots. The objective of this study is improving GA performance for efficient and fast selection in generating the Multi objective optimal path for mobile robot navigation in static environment. First we used the proposed approach to evaluate its ability to solve single objective problem in length term as well as we compared it with traditional ACO and simple GA then we extended to solve Pareto optimality ideas based on three criteria: length, smoothness and security, and making it Multi objective Hybrid approach. The proposed approach is tested to generate the single and multi objective optimal collision free path. The simulation results show that the mobile robot travels successfully from one location to another and reaches its goal after avoiding all obstacles that are located in its way in all tested environment and indicate that the proposed approach is accurate and can find a set Pareto optimal solution efficiently in a single run.
Authors: Bai Qiang You, Tian Zeng Huang, Jian Hua Zhou, Yu Han Chi, Tao Zhou, Hui Liang Liu
Abstract: A novel real-time distribute dynamic liquid measurement system is proposed with the method based on ideal gas state equation and wireless transmission technology. For the designed system, the constitution and working process, the sensitivity and stability, the error analysis of multi-objective synchronous acquisition, as well as improvement possibility are all discussed in detail. Critical parameters representing dynamic liquid volumes in distribute closed multi-objectives could be collected over some distance and resolved in the center of the system in real-time. The results show that the total accuracy of the designed system could be better than 2% within usable measurement range, if some key detectors were carefully selected.
Authors: Zhi Bin Qin, Zhao Hui Liu, Yu Zhi Li
Abstract: The concept of the road-region ecosystem is described and its environmental features analyzed. In addition, the stability of the road-region ecosystem is summarized. A scientific and rational evaluation of road-region ecosystem stability is proposed to properly investigate the relationship between highway construction and protection of the ecological environment. This paper investigated a method for determining an index system to be used to evaluate road-region ecosystem stability. It put forward an index system for assessing road-region ecosystem stability as a reference. On the basis of detailed analysis of the multidimensional space of the road-region ecosystem, a new method and calculation formula for multi-objective comprehensive evaluation of road-region ecosystem stability are presented. Computation result is fit with the actual situation. The result indicates that the evaluation index system and the method are feasible.
Authors: Qi Yang, Jing Shen, Xing Fan, Chao Li, Yang Shuo Shen, Guo He Huang, Li He
Abstract: During the groundwater has been suffered from varies degrees pollution. It needs an approach to help decision maker to determine remediation strategies. In this study, a groundwater multi-objective model that includes two objectives (i.e. total remediation cost and average concentration after remediation) is proposed and successfully applied to a petroleum-contaminated aquifer located in western Canada. It can determine a remediation strategy through optimal software MATLAB after convert multi-objective into a non-linear objective using α liner weighting method.
Authors: Meng Zhang, Guo Xi Li, Yue Hui Yan, Bao Zhong Wu
Abstract: The current product configuration methods can only be applied to the situation when the configuration information is specific or fuzzy. In order to address this problem, a new multi-objective optimization approach to configuration design with the consideration of several types of uncertain information was proposed. The uncertain configuration information was uniformly described with interval numbers. Targeting on optimizing the performance, cost and term of configured products, three mathematical models was established, and some adaptations were made to these models according to the interval number. A multi-objective optimization model was generated by integrating the three models. The non-dominated sorting genetic algorithm II was used to solve the model and a Pareto optimal set of product configuration schemes was obtained. A general optimum selection method was put forward based on the fuzzy set theory, and the optimization sequence of the Pareto solutions can be founded using the method. The proposed approach can effectively deal with the problem of product configuration optimization under uncertain information.
Authors: Cucuk Nur Rosyidi, Azizah Aisyati, Anggun Tri Kusumaningru
Abstract: In recent years, environmental issues such as global warming and ozone depletion become more intense. The issues have made many companies include the environmental performances into their product life cycle from design into disposal stage. In the design stage, it is important to determine the optimal dimensions of the product which balancing the mechanical and environmental performances. The aim of this research is to develop a multi-objective optimization model for plastic cup design using mechanical and environmental performances. A case study is given to show the implementation of the model. The optimization model resulted a thickness which is not significantly different comparing to the current practice in the company. The results give more efficient use of material and reduction of production cost and environmental impact.
Authors: Tian Qi Yang
Abstract: Based on the basic model of economic dispatch and energy-efficient power generation scheduling rules, the multi-objective optimization model of energy-efficient scheduling is given. The model is transformed into unconstrained optimization problem by means of penalty factor and the dynamic penalty function, and then solved by PSO algorithm. The results obtained demonstrate the rationality and effectiveness of the proposed model.
Authors: Zhao Xia Shang, Hong Liu
Abstract: This paper proposed a multiple-objective algorism based on target space partitioning and demonstrated its evolution and optimization. The result showed that this algorism is effective in keeping the even distribution and convergence of the solutions. This paper also proposed an innovative design idea for algorism computation – the concept of tolerance is introduced and by adopting the double-population storage mechanism, the algorism's overall optimization capability is improved.
Authors: D. Lakshmipathy, M. Chandrasekaran, T. Balamurugan, P. Sriramya
Abstract: The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.
Authors: Guo Hui Zhang, Liang Gao, Yang Shi
Abstract: Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. It is quite difficult to achieve optimal or near-optimal solutions with single traditional optimization approach because the multi objective FJSP has the high computational complexity. An novel hybrid algorithm combined variable neighborhood search algorithm with genetic algorithm is proposed to solve the multi objective FJSP in this paper. An external memory is adopted to save and update the non-dominated solutions during the optimization process. To evaluate the performance of the proposed hybrid algorithm, benchmark problems are solved. Computational results show that the proposed algorithm is efficient and effective approach for the multi objective FJSP.
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