Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
Multiple setup PCB assembly planning using genetic algorithms
Computers and Industrial Engineering
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Computers and Industrial Engineering
Multidisciplinary heat generating logic block placement optimization using genetic algorithm
Microelectronics Journal
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
Multi-objective design optimization of MCM placement
IMCAS'06 Proceedings of the 5th WSEAS international conference on Instrumentation, measurement, circuits and systems
A two-stage genetic algorithm for multi-objective job shop scheduling problems
Journal of Intelligent Manufacturing
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The optimal placement of electronic components on a printed circuit board (PCB) requires satisfying multiple conflicting design objectives as most of the components have different power dissipation, operating temperature, types of material and dimension. In addition, most electronic companies are currently emphasizing on designing a smaller package electronic system in order to increase the system performance. This paper presents a new self organizing genetic algorithm (SOGA) method for solving this multi-objective optimization problem. The SOGA can be viewed as a cascade of two GAs which consists of two steps fitness evaluation process to ensure that the fitness of selected chromosomes for each iteration process is optimally selected. The algorithm is developed based on weighted sum approach genetic algorithm (WSGA) where an inner loop GA is used to optimize the selection of weights of the WSGA. Experiments are conducted to evaluate the performance of SOGA. Four objective functions are formulated in the experiments which are temperature of components, area of PCB, high power component placement and high potential critical components distance. Comparisons of the performance of SOGA are made with two well known methods namely fixed weight GA (FWGA) and random weighted GA (RWGA). The results show that the SOGA gives a better optimal solution as compared to the other methods.