Advanced genetic algorithm to solve MINLP problems over GPU

Munawar, Asim; Wahib, Mohamed; Munetomo, Masaharu; Akama, Kiyoshi

doi:10.1109/cec.2011.5949635

Cited by 21 publications

(10 citation statements)

References 21 publications

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“…Problems solved by PGA on GPGPU are Medical Image Registration [23], Feature Selection, Electrical Circuit Synthesis and Data Mining [24], SAT Problems [25], Function Optimization [26], Benchmark Problems [27], [28], [29], Texture-Rendering [30], One-MAX Problem [31], Quadratic Assignment Problems [32], Non-convex Mixed Integer Non-Linear Programming (MINLP) and Non-convex Non Linear Programming (NLP) Problems [33], Cellular Automata Rules Acceleration [34], Stereo Matching [35], Data Mining [36], Drug discovery [37], Gaming Application [38]…”

Section: Ga Over Gpgpumentioning

confidence: 99%

Untitled

2013

IJSC

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Genetic Algorithm (GA), a stochastic optimization technique, doesn't ensure optimal solution every time. Nowadays there is a need to improve the performance of each and every application so that the time required for obtaining quality solution can be minimized. This paper gives a brief overview of theoretical advances and computing trends, particularly population diversity in PGA (Parallel GA) and provides information about how various authors, researchers, scientists have parallelized GA over various parallel computing paradigms viz. Cluster, MPP (Massively Parallel Processing), GPGPU (General purpose Graphics Processing Units), Grid, Cloud, Multicore/HPC to ensure more optimal solution every time with efficacy and efficiency.

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Section: Ga Over Gpgpumentioning

confidence: 99%

Untitled

2013

IJSC

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“…More recently, Graphic Processor Units (GPUs) have been used to implement GAs for solving mixed integer non-linear programming problems [12]. As GPUs have highly parallel structures, they become attractive for implementations of a cGA [13], where a processing element can exist for each solution, thus maximizing parallelization.…”

Section: B Hardware For Genetic Algorithmsmentioning

confidence: 99%

A scalable array for Cellular Genetic Algorithms: TSP as case study

Santos

Alves

Ferreira

2012

2012 International Conference on Reconfigurable Computing and FPGAs

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Cellular Genetic Algorithms (cGAs) exhibit a natural parallelism that makes them interesting candidates for hardware implementation, as several processing elements can operate simultaneously on subpopulations shared among them. This paper presents a scalable architecture for a cGA, suitable for FPGA implementation. A regular array of custom designed processing elements (PEs) works on a population of solutions that is spread into dual-port memory blocks locally shared by adjacent PEs. A travelling salesman problem with 150 cities was used to verify the implementation of the proposed cGA on a Virtex-6 FPGA, using a population of 128 solutions with different levels of parallelism (1, 4, 16 and 64 PEs). Results have shown that an increase of the number of PEs does not degrade the quality of the convergence of the iterative process, and that the throughput increases almost linearly with the number of PEs. Comparing with a software implementation running in a PC, the cGA with 64 PEs has shown a 45x speedup.

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“…For example, in design optimization, the design objective could be simply to minimize the cost or maximize the efficiency of production; on the other hand, the objective could be more complex, e.g., controlling the highly non-linear behavior of pH neutralization processes in a chemical plant. The need to solve practical NLP/MINLP problems has led to the development of a large number of heuristics and metaheuristics over the last two decades [27,33,36]. Metaheuristics, which are emerging as effective alternatives for solving NP-hard optimization problems, are strategies for designing or improving very general heuristic procedures with high performance in order to find (near-)optimal solutions; the goal is efficient exploration (diversification) and exploitation (intensification) of the search space.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design

Peraza-Vázquez

Torres‐Huerta

Flores‐Vela

2016

CyS

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The paper presents a differential evolution (DE)-based hyper-heuristic algorithm suitable for the optimization of mixed-integer non-linear programming (MINLP) problems. The hyper-heuristic framework includes self-adaptive parameters, an ε-constrained method for handling constraints, and 18 DE variants as low-level heuristics. Using the proposed approach, we solved a set of classical test problems on process synthesis and design and compared the results with those of several state-of-the-art evolutionary algorithms. To verify the consistency of the proposed approach, the above-mentioned comparison was made with respect to the percentage of convergences to the global optimum (NRC) and the average number of objective function evaluations (NFE) over several trials. Thus, we found that the proposed methodology significantly improves performance in terms of NRC and NFE.

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Advanced genetic algorithm to solve MINLP problems over GPU

Cited by 21 publications

References 21 publications

Untitled

Untitled

A scalable array for Cellular Genetic Algorithms: TSP as case study

Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design

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