A Modified Sequential Particle Swarm Optimization Algorithm with Future Time Data for Solving Transient Inverse Heat Conduction Problems

Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems

“…It has been used widely for various optimization problems like machining [49], solidification [50] and heat exchanger optimization [51][52][53]. Recently, it is also used for inverse heat transfer problems involving conduction [20,21,[54][55][56], convection [57] and radiation [58,59]. It is evident from the literature that PSO enjoys surge in popularity among heat transfer community than many other search based algorithms.…”

“…Hence, these methods are more robust. Computational time is of course a concern for these methods [16] but with ever growing computational power, parallelization [19], modifications [20][21][22] and development of hybrid methods [23][24][25], these methods can substitute http://dx.doi.org/10.1016/j.ijheatmasstransfer.2015.05.015 0017-9310/Ó 2015 Elsevier Ltd. All rights reserved. the conventional methods.…”

Optimization of highly conductive insert architecture for cooling a rectangular chip

“…On the other hand, modern heat conduction methods have been largely investigated to solve applicable problems. For instance, the particle swarm optimization (PSO) method is used to solve inverse heat conduction problems [16][17][18]. Additionally, a numerical technique for tracking interfaces and shapes called level set method is developed for nonlinear heat conduction problems [19,20].…”

Particle Swarm Optimization Algorithm with Chaotic Mapping Model

“…The behind calculation data are based on the initial population. In the selection of the initial population, if the initial value are selected in the vicinity of the global optimum solution, the iterative searching algorithm can search the global optimal solution in a very short period of time [10,11]. On the contrary, the improper selection of initial population is likely to cause the algorithm in local optimum.…”