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micro genetic algorithm with spatial crossover and correction schemes for constrained three-dimensional reader network planning

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 44卷 344-353页

作者： Lin, Shin-Yeu Tsai, Hsing-Fang Chang Gung Univ Dept Elect Engn 259 Wen Hwa 1st Rd Taoyuan 333 Taiwan

Due to the fast growing electronic commerce, the constrained three-dimensional reader network planning (C3DRNP) of the radio frequency identification (RFID) system for large warehouses is a subject that is worthy of study. A micro genetic algorithm (mGA) with novel spatial crossover and correction schemes is proposed to cope with this C3DRNP problem. The proposed algorithm is computationally efficient, which allows a frequent replacement of the RFID readers in the network to account for the fast turnaround time of the stored objects in the warehouse, and guarantees 100% tag coverage to avoid missing the records of the objects. The proposed algorithm is tested and compared with the existing methods such as the particle swarm optimization (PSO) method and the conventional GA (CGA) on solving several C3DRNP problems with various network sizes. The comparison results demonstrate the computational efficiency of the mGA and the effectiveness of the novel spatial crossover and correction schemes in searching the solution. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Radio frequency identification (RFID) RFID reader network planning micro genetic algorithm Spatial crossover Correction scheme

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micro genetic algorithm based optimal gate positioning in injection molding design

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2007年第5期21卷 789-798页

作者： Lee, Jongsoo Kim, Jonghun Yonsei Univ Sch Mech Engn Seoul 120749 South Korea

The paper deals with the optimization of runner system in injection molding design. The design objective is to locate gate positions by minimizing both maximum injection pressure at the injection port and maximum pressure difference among all the gates on a product with constraints on shear stress and/or weld-line. The analysis of filling process is conducted by a finite element based program for polymer flow. micro genetic algorithm (mGA) is used as a global optimization tool due to the nature of inherent nonlinearlity in flow analysis. Four different design applications in injection molds are explored to examine the proposed design strategies. The paper shows the effectiveness of mGA in the context of optimization of runner system in injection molding design.

关键词： micro genetic algorithm design optimization filling injection mold

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Approximating Arbitrary Reinforcement Signal by Learning Classifier Systems using micro genetic algorithm

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FUNDAMENTA INFORMATICAE 2008年第1-2期86卷 93-111页

作者： Hamzeh, Ali Rahmani, Adel Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

Learning Classifier Systems are Evolutionary Learning mechanisms which combine genetic algorithm and the Reinforcement Learning paradigm. Learning Classifier Systems try to evolve state-action-reward mappings to propose the best action for each environmental state to maximize the achieved reward. In the first versions of learning classifier systems, state-action pairs can only be mapped to a constant real-valued reward. So to model a fairly complex environment, LCSs had to develop redundant state-action pairs which had to be mapped to different reward values. But an extension to a well-known LCS, called Accuracy Based Learning Classifier System or XCS, was recently developed which was able to map state-action pairs to a linear reward function. This new extension, called XCSF, can develop a more compact population than the original XCS. But some further researches have shown that this new extension is not able to develop proper mappings when the input parameters are from certain intervals. As a solution to this issue, in our previous works, we proposed a novel solution inspired by the idea of using evolutionary approach to approximate the reward landscape. The first results seem promising, but our approach, called XCSFG, converged to the goal very slowly. In this paper, we propose a new extension to XCSFG which employs micro-GA which its needed population is extremely smaller than simple GA. So we expect micro-GA to help XCSFG to converge faster. Reported results show that this new extension can be assumed as an alternative approach in XCSF family with respect to its convergence speed, approximation accuracy and population compactness.

关键词： Function Approximation Learning Classifier Systems micro genetic algorithm XCSF

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A Modified micro genetic algorithm for undertaking Multi-Objective Optimization Problems

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2013年第3期24卷 483-495页

作者： Tan, Choo Jun Lim, Chee Peng Cheah, Yu-N Univ Sci Malaysia Sch Comp Sci George Town Malaysia Deakin Univ Ctr Intelligent Syst Res Burwood Vic Australia

In this paper, a Modified micro genetic algorithm (MmGA) is proposed for undertaking Multi-objective Optimization Problems (MOPs). An NSGA-II inspired elitism strategy and a population initialization strategy are embedded into the traditional micro genetic algorithm (mGA) to form the proposed MmGA. The main aim of the MmGA is to improve its convergence rate towards the pareto optimal solutions. To evaluate the effectiveness of the MmGA, two experiments using the Kursawe test function in MOPs are conducted, and the results are compared with those from other approaches using a multi-objective evolutionary algorithm indicator, i.e. the Generational Distance (GD). The outcomes positively demonstrate that the MmGA is able to provide useful solutions with improved GD measures for tackling MOPs.

关键词： Multi-objective optimisation micro genetic algorithm non-dominated sorting genetic algorithm-II elitism strategy population initialisation strategy

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An efficient multi-objective optimization approach based on the micro genetic algorithm and its application

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INTERNATIONAL JOURNAL OF MECHANICS AND MATERIALS IN DESIGN 2012年第1期8卷 37-49页

作者： Liu, G. P. Han, X. Jiang, C. Hunan Univ State Key Lab Adv Design Mfg Vehicle Body Coll Mech & Automot Engn Changsha 410082 Hunan Peoples R China

In this paper, an efficient multi-objective optimization approach based on the micro genetic algorithm is suggested to solving the multi-objective optimization problems. An external elite archive is used to store Pareto-optimal solutions found in the evolutionary process. A non-dominated sorting is employed to classify the combinational population of the evolutionary population and the external elite population into several different non-dominated levels. Once the evolutionary population converges, an exploratory operator will be performed to explore more non-dominated solutions, and a restart strategy will be subsequently adopted. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to NSGAII. Eventually, this approach is applied to the structural optimization of a composite laminated plate for maximum stiffness in thickness direction and minimum mass.

关键词： Multi-objective optimization micro genetic algorithm Non-dominated sorting Laminated plates

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Spatial analysis for functional region of suburban-rural area using micro genetic algorithm with variable population size

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第7期39卷 6469-6475页

作者： Chen, Yi Song, Zhi-Jun China Agr Univ Coll Resource & Environm Beijing 100193 Peoples R China Univ Elect Sci & Technol China Sch Mechatron Engn Chengdu 611731 Peoples R China Univ Glasgow Dept Mech Engn Glasgow G12 8QQ Lanark Scotland Capital Univ Econ & Business Beijing 101500 Peoples R China

A micro genetic algorithm with variable population size (VP mu GA) is proposed for the spatial analysis for the functional region of suburan-rural area, in which, the fitness function is implied by a functional region affecting index (Theta) with a 'law-of-gravity' interpretation. The VP mu GA evaluates the Theta represented dynamical behaviours over a 'short' to 'long' term period, which also revisits the urbanisation of Beijing and examines the Theta sensitivity to the functional distance of 13 suburban-rural districts. Numerical results with given statistics has been obtained using a specially devised simulation toolkit, it is shown that the VP mu GA method can work valuably as a tool for providing a functional distanced based estimation of the inter-relationships between the enterprises number, the regional profit, the local population, the regional employment, etc., and to use this understanding to evaluate suburban-rural districts that are more resilient and adaptable. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Spatial analysis genetic algorithms micro genetic algorithm Functional region Suburban area Rural area

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Damage detection of truss structures using two-stage optimization based on micro genetic algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2014年第9期28卷 3687-3695页

作者： Kim, Nam-Il Kim, Hyungmin Lee, Jaehong Sejong Univ Dept Architectural Engn Seoul 143747 South Korea

A simple and efficient two-stage optimization procedure is proposed to properly identify the sites and the extent of multiple damages in truss structures. In the first stage, the most potentially damaged elements are identified using an anti-optimization (AO) technique. In the second stage, a micro genetic algorithm (MGA) is performed to accurately determine the actual damage extents based on a priori knowledge from the first stage. The correctness and effectiveness of the proposed algorithm are proved by two illustrated test examples: the planar and space truss models with the numerically simulated data. The numerical results show the computational advantages of the proposed method to precisely determine the sites and the extent of multiple damages of truss structures.

关键词： Damage detection Anti-optimization micro genetic algorithm Truss structures Natural frequency

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Gate positioning design of injection mould using bi-objective micro genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2008年第6期222卷 687-699页

作者： Lee, J. Lee, J. Yonsei Univ Sch Mech Engn Seoul 120749 South Korea

The use of a micro genetic algorithm (mGA)-based approach to solve a bi-objective optimization of an injection mould design problem is presented. The advantage of the mGA-based approach is that it requires fewer computational resources than a conventional GA because it has a smaller population than a conventional GA. The main drawback of the mGA-based approach is that design diversity is not secured when multi-modal and multi-objective designs are investigated. To implement the mGA-based bi-objective optimization procedure, the present study proposes a memory set, a filtering process, weight control, and reproduction from the memory set in order to explore new optimal solutions, and identify more-evenly distributed Pareto surfaces. A number of mathematical functions and a typical structural optimization problem are tested to verify the proposed strategies. The approach is subsequently applied to the bi-objective injection moulding design problem of minimizing both the maximum injection pressure and maximum pressure difference between the gate positions in the runner system.

关键词： bi-objective optimization micro genetic algorithm pareto fronts weight control

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Optimization of combustion chamber geometry for stoichiometric diesel combustion using a micro genetic algorithm

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FUEL PROCESSING TECHNOLOGY 2010年第11期91卷 1742-1752页

作者： Park, Sung Wook Hanyang Univ Sch Mech Engn Seoul 133791 South Korea

This paper describes the optimization of combustion chamber geometry and engine operating conditions for stoichiometric diesel combustion, targeting lower gross indicated specific fuel consumption. The KIVA code, coupled with a micro genetic algorithm population of nine for each generation was used. The optimization variables were composed of ten variables related to the combustion chamber geometry and engine operating conditions. In addition, an auto mesh generator was developed for generating various kinds of combustion chambers, such as open-crater, re-entrant, deep, and shallow types. In addition, the computational models were validated against the experimental results for a stoichiometric process in terms of the combustion pressure history and emissions. Through the preset optimization, a 35% improvement in the gross indicated that specific fuel consumption was achieved. In addition, the optimization results showed that the optimum engine operating conditions employed a premixed charge compression ignition combustion regime with early injection and a narrow spray included angle. Furthermore, a higher boost pressure was used to prevent fuel film formation. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Combustion chamber geometry micro genetic algorithm KIVA code Stoichiometric diesel combustion

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Applying micro genetic algorithm to numerical model for luminous intensity distribution of planar prism LED luminaire

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OPTICS COMMUNICATIONS 2013年 293卷 22-30页

作者： Kim, Yu-Sin Choi, An-Seop Jeong, Jae-Weon Sejong Univ Dept Architectural Engn Seoul 143747 South Korea Hanyang Univ Dept Architectural Engn Seoul 133791 South Korea

Several technological problems have to be resolved for LED to be used as a general purpose light source. For instance, LED luminaires have a property of high luminance due to a narrow beam angle and a small volume compared to general light sources. There are several differences between existing luminaires and the intensity distribution of an LED luminaire. An optical engineer has the challenging problem of designing a spatially extended and non-uniform light source. It is difficult for LED luminaires to obtain the target luminous intensity distribution required in a space, because the design process requires much trial and error. And the process also requires much design time. In this study, an optimization algorithm of a numerical model for luminous intensity distribution of planar LED luminaires was developed to find optimized prism angles. This algorithm would used the micro genetic algorithm technique which is the improved form of genetic algorithms. Also, this algorithm is capable of deriving the optimized angle of a prism to achieve the target luminous intensity distribution of planar prism LED luminaires. Therefore, the purpose of this study is to introduce a numerical model of a planar prism LED luminaire and to develop a optimization algorithm that is able to find an optimum luminaire optical design solution using the micro genetic algorithm. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Optimization algorithm Numerical model of luminous intensity distribution micro genetic algorithm Optical design Planer LEDs prism luminaire

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