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What is Computational Effort

Chapter 4
Handbook of Research on Computational Simulation and Modeling in Engineering
While meta-heuristic produce superior solutions are important, the speed of computation is a key factor. There are many portions of the process should be time, including time to best-found solution, total running time and time per phase. In this research, total running time is focused more than the rest. This is the algorithm’s execution time prior to termination by its stopping condition.
Published in Chapter:
Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution: MO Optimization of TEC Using DE
Doan V. K. Khanh (Universiti Teknologi PETRONAS, Malaysia), Pandian Vasant (Universiti Teknologi PETRONAS, Malaysia), Irraivan Elamvazuthi (Universiti Teknologi PETRONAS, Malaysia), and Vo N. Dieu (HCMC University of Technology, Vietnam)
Copyright: © 2016 |Pages: 32
DOI: 10.4018/978-1-4666-8823-0.ch004
Abstract
In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.
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