Hardware based genetic evolution of self-adaptive arbitrary response FIR filters

Shoaib, Mohammed; Mahammad, S. K. Noor; Kamakoti, V.

doi:10.1016/j.asoc.2010.01.004

Cited by 2 publications

(1 citation statement)

References 21 publications

(27 reference statements)

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“…A significant lower number of FPGA implementations of parallel metaheuristics have been proposed in the last 5 years, mainly because they provide less flexibility than the new hardware platforms for software‐based implementations of metaheuristics such as GPUs. FPGAs have been used when specific real‐time requirements to find the solution for an optimization problem are formulated (Allaire et al., ; Walton et al., ), or just to achieve an improved efficiency (Farmahini‐Farahani et al., ; Huang et al., ; Mohammed et al., ). The cellular model for parallel metaheuristics has been the most popular choice to implement in FPGAs, because the logical elements and the reprogrammable interconnection make it easy to implement its population organization and interactions (Fernando et al., ; Jewajinda and Chongstitvatana, ).…”

Section: Technologies For Parallel Metaheuristicsmentioning

confidence: 99%

Parallel metaheuristics: recent advances and new trends

Alba

Luque

Nesmachnow

2012

Int Trans Operational Res

268

118

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The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.

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