A Multiobjective Optimization Method for the SOC Test Time, TAM, and Power Optimization Using a Strength Pareto Evolutionary Algorithm

2017 ◽  
pp. 685-695 ◽  
Author(s):  
Wissam Marrouche ◽  
Rana Farah ◽  
Haidar M. Harmanani
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Power Optimization ◽  
Optimization Method ◽  
Test Time ◽  
Soc Test ◽  
Strength Pareto Evolutionary Algorithm

A Strength Pareto Evolutionary Algorithm for Optimizing System-On-Chip Test Schedules

2018 ◽  
Vol 17 (02) ◽  
pp. 1850010
Author(s):  
Wissam Marrouche ◽  
Rana Farah ◽  
Haidar M. Harmanani
Keyword(s):  
Evolutionary Algorithm ◽  
System On Chip ◽  
Test Scheduling ◽  
Scheduling Problem ◽  
Single Chip ◽  
Simultaneous Test ◽  
Soc Test ◽  
Strength Pareto Evolutionary Algorithm ◽  
Tightly Coupled ◽  
On Chip

System-on-chip (SOC) has become a mainstream design practice that integrates intellectual property cores on a single chip. The SOC test scheduling problem maximizes the simultaneous test of all cores by determining the order in which various cores are tested. The problem is tightly coupled with the test access mechanism (TAM) bandwidth and wrapper design. This paper presents a strength Pareto evolutionary algorithm for the SOC test scheduling problem with the objective of minimizing the power-constrained test application time, wrapper design and TAM assignment in flat and hierarchical core-based systems. We demonstrate the effectiveness of the method using the ITC’02 benchmarks.

scholarly journals Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Yanyan Tan ◽  
Xue Lu ◽  
Yan Liu ◽  
Qiang Wang ◽  
Huaxiang Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multiobjective Optimization Problems ◽  
Distinct Features

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

Application of Evolutionary Algorithm for Triobjective Optimization

2014 ◽  
Vol 3 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Naourez Ben Hadj ◽  
Jalila Kaouthar Kammoun ◽  
Rafik Neji
Keyword(s):  
Energy Consumption ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Permanent Magnets ◽  
Fitness Function ◽  
Vehicle Performance ◽  
Strength Pareto Evolutionary Algorithm ◽  
Simulation Results

For Electric Vehicles (EVs), Weight and losses reduction are important factors not only in reducing the energy consumption and cost but also in increasing autonomy. This paper describes the application of an evolutionary algorithm for multiobjective optimization in the traction chain (TC) of pure EV. In this study, the optimisation algorithm is based on the Strength Pareto Evolutionary Algorithm (SPEA-II) and the fitness function is defined so as to minimize the electric vehicle cost (EVC), the electric vehicle weight (EVW) and the losses in the electric vehicle (EVL). Also, in this study, different requirements are considered as constraints like the efficiency of the permanent magnets engine, the number of conductor in the slots, the winding temperature…The simulation results show the effectiveness of the approach and reduction in EVC, EVW and EVL while ensuring that the electric vehicle performance is not sacrificed.

scholarly journals A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wanxing Sheng ◽  
Ke-yan Liu ◽  
Yongmei Liu ◽  
Xiaoli Meng ◽  
Xiaohui Song
Keyword(s):  
Multiobjective Optimization ◽  
Power System ◽  
Evolutionary Algorithm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Optimal Operation ◽  
Penalty Factor ◽  
Adaptive Crossover ◽  
Comparison Of The Results

A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2), a particle swarm optimization (PSO) algorithm, and nondominated sorting genetic algorithm II (NGSA-II). The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

3D SoC test optimization method based on balance of scan chain

2013 ◽  
Vol 26 (7) ◽  
pp. 586-590
Author(s):  
Wei Wang ◽  
Xin Li ◽  
Tian Chen ◽  
Jun Liu ◽  
Fang Fang ◽  
...  
Keyword(s):  
Optimization Method ◽  
Soc Test ◽  
Scan Chain ◽  
Test Optimization

Multiobjective Optimization of the Heating Process for Forging Automotive Crankshaft

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Hong-Seok Park ◽  
Xuan-Phuong Dang
Keyword(s):  
Multiobjective Optimization ◽  
Heating System ◽  
Optimization Method ◽  
Heating Process ◽  
Temperature Deviation ◽  
Engineering Data ◽  
Optimization Of Process Parameters ◽  
Mathematical Approximation ◽  
Nondominated Sorting ◽  
Induction Heating System

This paper presents potential approaches that increase the energy efficiency of an in-line induction heating system for forging of an automotive crankshaft. Both heat loss reduction and optimization of process parameters are proposed scientifically in order to minimize the energy consumption and the temperature deviation in the workpiece. We applied the numerical multiobjective optimization method in conjunction with the design of experiment (DOE), mathematical approximation with metamodel, nondominated sorting genetic algorithm (GA), and engineering data mining. The results show that using the insulating covers reduces heat by an amount equivalent to 9% of the energy stored in the heated workpiece, and approximately 5.8% of the energy can be saved by process parameter optimization.

HYBRIDIZATION OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHM WITH COEVOLUTION FOR ENEMY TEAM IN MS. PAC-MAN GAME

2014 ◽  
Vol 2 (1) ◽  
pp. 36-51
Author(s):  
Tse guan Tan ◽  
Jason Teo ◽  
On Chin Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Dynamic Game ◽  
Evolution Strategy ◽  
Evolutionary Multiobjective Optimization ◽  
Competitive Coevolution ◽  
Virtual Creatures

AbstrakKini, semakin ramai penyelidik telah menunjukkan minat mengkaji permainan Kecerdasan Buatan (KB).Permainan seumpama ini menyediakan tapak uji yang sangat berguna dan baik untuk mengkaji asasdan teknik-teknik KB. Teknik KB, seperti pembelajaran, pencarian dan perencanaan digunakan untukmenghasilkan agen maya yang mampu berfikir dan bertindak sewajarnya dalam persekitaran permainanyang kompleks dan dinamik. Dalam kajian ini, satu set pengawal permainan autonomi untuk pasukan hantudalam permainan Ms. Pac-man yang dicipta dengan menggunakan penghibridan Evolusi PengoptimumanMultiobjektif (EPM) dan ko-evolusi persaingan untuk menyelesaikan masalah pengoptimuman dua objektifiaitu meminimumkan mata dalam permainan dan bilangan neuron tersembunyi di dalam rangkaianneural buatan secara serentak. Arkib Pareto Evolusi Strategi (APES) digunakan, teknik pengoptimumanmultiobjektif ini telah dibuktikan secara saintifik antara yang efektif di dalam pelbagai aplikasi. Secarakeseluruhannya, keputusan eksperimen menunjukkan bahawa teknik pengoptimuman multiobjektif bolehmendapat manfaat daripada aplikasi ko-evolusi persaingan Abstract Recently, researchers have shown an increased interest in game Artificial Intelligence (AI). Gamesprovide a very useful and excellent testbed for fundamental AI research. The AI techniques, such aslearning, searching and planning are applied to generate the virtual creatures that are able to think andact appropriately in the complex and dynamic game environments. In this study, a set of autonomousgame controllers for the ghost team in the Ms. Pac-man game are created by using the hybridizationof Evolutionary Multiobjective Optimization (EMO) and competitive coevolution to solve the bi-objectiveoptimization problem of minimizing the game's score by eating Ms. Pac-man agent and the number ofhidden neurons in neural network simultaneously. The Pareto Archived Evolution Strategy (PAES) is usedthat has been proved to be an effective and efficient multiobjective optimization technique in variousapplications. Overall, the results show that multiobjective optimizer can benefit from the application ofcompetitive coevolutionary

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