High-performance computing memetic algorithm (HPCMA) model to process image fingerprint dataset

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

PurposeThe paper aims to present a new methodology for hybrid genetic algorithms (GA) in the solution of electromagnetic optimization problems.Design/methodology/approachThis methodology can be seen as a local search operator which uses local quadratic approximations for each objective and constraint function in the problem. In the local search phase, these approximations define an associated local search problem that is efficiently solved using a formulation based on linear matrix inequalities.FindingsThe paper illustrates the proposed methodology comparing the performance of the hybrid GA against the basic GA in two analytical problems and in the well‐known TEAM benchmark Problem 22. For the analytical problems, 30 independent runs for each algorithm were considered whereas for Problem 22, ten independent runs for each algorithm were taken.Research limitations/implicationsFor the analytical problems, the hybrid GA enhanced both the convergence speed, in terms of the number of function evaluations, and the accuracy of the final result. For Problem 22, the hybrid GA was able to reach a better solution, with a better value of the standard deviation with less CPU time.Practical implicationsThe paper could be useful both for device designers and researchers involved optimization in computational electromagnetics.Originality/valueThe hybrid GA proposed enhanced the convergence speed, in terms of the number of function evaluations, representing a faster and robust algorithm for practical optimization problems.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

Usually, scientific applications outlive the lifespan of the High Performance Computing (HPC) systems for which they are initially developed. The innovations in the HPC systems’ hardware and parallel programming standards drive the modernization of HPC applications so that they continue being performant. While such code modernization efforts may not be challenging for HPC experts and well-funded research groups, many domain-experts and students may find it challenging to adapt their applications for the latest HPC systems due to lack of expertise, time, and funds. The challenges of such domain-experts and students can be mitigated by providing them high-level tools for code modernization and migration. A brief overview of two such high-level tools is presented in this chapter. These tools support the code modernization and migration efforts by assisting users in parallelizing their applications and porting them to HPC systems with high-bandwidth memory. The tools are named as: Interactive Parallelization Tool (IPT) and Interactive Code Adaptation Tool (ICAT). Such high-level tools not only improve the productivity of their users and the performance of the applications but they also improve the utilization of HPC resources.

This paper presents an automated optimal design method using a hybrid genetic algorithm for pile group foundation design. The design process is a sizing and topology optimization for pile foundations. The objective is to minimize the material volume of the foundation taking the configuration, number, and cross-sectional dimensions of the piles as well as the thickness of the pile cap as design variables. A local search operator by the fully stressed design (FSD) approach is incorporated into a genetic algorithm (GA) to tackle two major shortcomings of a GA, namely, large computation effort in searching the optimum design and poor local search capability. The effectiveness and capability of the proposed algorithm are first illustrated by a five by five pile group subjected to different loading conditions. The proposed optimization algorithm is then applied to a large-scale foundation project to demonstrate the practicality of the algorithm. The proposed hybrid genetic algorithm successfully minimizes the volume of material consumption and the result matches the engineering expectation. The FSD operator has great improvement on both design quality and convergence rate. Challenges encountered in the application of optimization techniques to design of pile groups consisting of hundreds of piles are discussed.

In recent years, High Performance Computing (HPC) systems have been shifting from expensive massively parallel custom architectures to clusters of commodity personal computers to take advantage of cost and performance benefits. To avoid having to restart an application in case of sudden failure, checkpointing/restart fault tolerance mechanisms are commonly implemented. One drawback to checkpointing/restart is that it creates an overhead which increases the execution time of an application. We present a theoretical analysis of our technique. The results show that the PLR checkpointing/restart can significantly improve the overall reliability of an HPC system.

Mechanical sizing of a composite launcher structure by hybridizing a genetic algorithm with a local search method

Memetic algorithms (MAs) are widely recognized to have better convergence capability than their conventional counterparts. Due to its good robustness and universality, differential evolution (DE) has been frequently used as the global search method in MAs. However, on account of the limited performance of the conventional local search operators, the performance of previous DE-related MAs still needs further improvement. In this paper, we implement more efficient evolutionary algorithms (EAs) as the local search techniques in an adaptive MA framework to form two MA(DE-LS) variants, and investigate their impacts. In order to comprehensively show the effectiveness and efficiency of MA(DE-LS), we experimentally compare it with state-of-the-art EAs, DE-based MAs and other MAs.

We aim to suggest a simple genetic algorithm (GA) and other four hybrid GAs (HGAs) for solving the asymmetric distance-constrained vehicle routing problem (ADVRP), a variant of vehicle routing problem (VRP). The VRP is a difficult NP-hard optimization problem that has numerous real-life applications. The VRP aims to find an optimal tour that has least total distance (or cost) to provide service to n customers (or nodes or cities) utilizing m vehicles so that every vehicle starts journey from and ends journey at a depot (headquarters) and visits every customer only once. The problem has many variations, and we consider the ADVRP for this study, where distance traveled by every vehicle must not exceed a predefined maximum distance. The proposed GA uses random initial population followed by sequential constructive crossover and swap mutation. The HGAs enhance the initial solution using 2-opt search method and incorporate a local search technique along with an immigration procedure to obtain effective solution to the ADVRP. Experiments have been conducted among the suggested GAs by solving several restricted and unrestricted ADVRP instances on asymmetric TSPLIB utilizing several vehicles. Our experiments claim that the suggested HGAs using local search methods are very effective. Finally, we reported a comparative study between our best HGA and a state-of-the-art algorithm on asymmetric capacitated VRP and found that our algorithm is better than the state-of-the-art algorithm for the instances.

In high performance computing (HPC) systems, optical network links are often utilized for the HPC networks of these systems, but they have a relatively high rate of failure compared to their electrical counterparts. Due to the high link failure rate, evaluating the impact of these failures on HPC workloads is of particular interest. We extended the Merlin network module of the Structural Simulation Toolkit (SST) in order to evaluate the impact of link failures on applications running on HPC systems which use dragonfly network topologies.We focus on dragonfly topologies as they are frequently found in HPC systems, including NERSC Cori and Edison systems.We demonstrate our changes to SST by providing a sample of performance results and routing statistics for a dragonfly network of 8,192 nodes and three HPC workloads with 1% of optical link failures. For the three motifs under consideration, we show that the impact of link failure is largely dependent on the underlying workloads running on the system.

The multiobjective time-dependent route planning problem is a hard multiobjective combinatorial optimization problem. Metaheuristics showed success in solving many hard optimization problems and recently many efforts have been directed to hybridize elements from different metaheuristics and search methods. The hybridization of genetic algorithms and local search methods proved to be successful in many domains. In this paper we present a genetic local search algorithm for solving the multiobjective time-dependent route planning problem taking the multiobjective route planning in dynamic multihop ridesharing as an example problem. The behavior of the proposed algorithm is compared, on two problem instances using a set of widely used quality indicators, with the behavior of a genetic algorithm proposed for solving the same problem. Experimentation results indicated that the proposed algorithm outperforms the genetic algorithm regarding all quality indicators.

Many applications use a combination of a local optimum search (LOS) and a genetic algorithm (GA), called a hybrid genetic algorithm (HGA), to solve problems. This hybrid can improve the performance of finding optimum solutions, but the HGA may produce redundancy when applying an LOS to inappropriate populations. This redundancy is the cause of high computation time, and it generates premature convergence and decreases HGA performance. This research therefore aims to reduce redundant LOS in HGAs. We propose a new technique called diversity selection (DS) and measure redundancy when applying an LOS in HGA. In this work, the DS selects appropriate populations to which to apply an LOS. The experiment then compares DS with other HGAs on numerical optimization problems. In addition, the HGAs were tested on two LOSs, a Nelder-Mead method and a quasi-Newton method to compare the speed of finding the optimum point in an LOS. The experimental results show that DS was able to quickly find the optimum point and give fewer redundant LOSs than other HGAs.

The Linear Ordering problem (LOP) is an NP-hard problem, which has been solved using different metaheuristic approaches. The best solution for this problem is a memetic algorithm, which uses the traditional approach of hybridizing a genetic algorithm with a single local search; on the contrary, in this paper we present a memetic solution hybridized with multiple local searches through all the memetic process. Experimental results show that using the best combination of local searches, instead of a single local search, the performance for XLOLIB instances is improved by 11.46% in terms of quality of the solution. For the UB-I instances, the proposed algorithm obtained a 0.12% average deviation from the best known solutions, achieving 17 new best known solutions. A Wilcoxon test was performed, ranking the proposed memetic algorithm as the second best solution of the state of the art for LOP. The results show that the multiple local searches approach can be more effective to get a better control in balancing intensification/diversification than the single local search approach.

Genetic Algorithm (GA) is a population-based stochastic optimizer that mimics natural selection to solve complex, non-differentiable, and multimodal problems. This paper first reviews the biological foundations and canonical operators- selection, crossover and mutation--before surveying major variants such as Hybrid GA, Parallel GA and the Predator--Prey Dynamic GA that inject local search, island parallelism or ecological balance to mitigate premature convergence. This paper then benchmarks these algorithms on continuous function optimisation and combinatorial tasks, using the sphere function and the travelling-salesman problem as illustrative cases. Results show that Hybrid GA with simulated-annealing local search achieves the best accuracy--speed trade-off, while PDGA offers the most stable evolution curve in high-dimensional multimodal landscapes. Finally, this paper identifies remaining challenges--expensive fitness evaluation, parameter sensitivity and interpretability--and outline future directions including surrogate-assisted evolution, GPU/quantum acceleration and lifelong on-device adaptation. The study confirms that GA, when properly hybridised and parallelised, remains a competitive backbone for next-generation AI systems.

This paper proposed and implemented an genetic and weed invasion hybrid algorithm in Sparse Array Synthesis. The classical weed invasion algorithm has high requirements on the convergence parameters in the process of generating weeds from seeds. Inappropriate parameters can easily lead to local convergence of evolution. The mutation and crossover operations similar to the genetic algorithm were introduced into the classical weed invasion algorithm. The method improves convergence speed, avoids local convergence, and enhances the stability of the algorithm. The algorithm is used in practical application in the synthesis of linear sparsely distributed array pattern, and the predetermined index is obtained through calculation. At the same time, compared with the genetic algorithm and the classical weed invasion algorithm, the results show that the hybrid algorithm has certain advantages.