A Novel Genetic Algorithm Based Scheduling for Multi-core Systems

Bose, Aditi; Biswas, Tarun; Kuila, Pratyay

doi:10.1007/978-981-13-2414-7_5

Cited by 29 publications

(24 citation statements)

References 14 publications

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“…In order to analyze the optimization results obtained from the GMBO, these results were compared with the performance of eight other optimization algorithms, including (i) famous methods: Genetic Algorithm (GA) [20], Particle Swarm Optimization (PSO) [10], (ii) popular methods: Gravitational Search Algorithm (GSA) [16], Teaching-Learning-Based Optimization (TLBO) [21], Grey Wolf Optimizer (GWO) [11], Whale Optimization Algorithm (WOA) [12], and (iii) recently methods: Tunicate Swarm Algorithm (TSA) [13] and Marine Predators Algorithm (MPA) [14]. The experimentation was done on MATLAB (R2020a version) using a 64-bit Core i7 processor of 3.20 GHz and 16 GB of main memory.…”

Section: Simulation Study and Resultsmentioning

confidence: 99%

“…Evolutionary-based optimization algorithms are developed based on the simulation of the laws of evolution and the concepts of genetics science. The Genetic Algorithm (GA) is the most famous and widely used evolutionary-based optimization algorithm, which was developed based on the simulation of reproduction and Darwin's theory of evolution [20]. The concepts of the GA are very simple and, therefore, can be easily implemented in solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

2021

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There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

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Section: Simulation Study and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

2021

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“…In order to analyze the quality of the results obtained from the proposed algorithm, these results have been compared with the performance of eight other well-known algorithms: Particle Swarm Optimization (PSO) [ 13 ], the Genetic Algorithm (GA) [ 12 ], Teaching–Learning-Based Optimization (TLBO) [ 15 ], the Gravitational Search Algorithm (GSA) [ 14 ], the Whale Optimization Algorithm (WOA) [ 17 ], the Grey Wolf Optimizer (GWO) [ 16 ], the Tunicate Swarm Algorithm (TSA) [ 19 ], and the Marine Predators Algorithm (MPA) [ 18 ]. The simulation results of the performance and implementation of the TOA and compared optimization algorithms on the mentioned twenty-three objective functions are shown using two indicators of the average of the obtained best solutions (ave.) and the standard deviation of the obtained best solutions (std.).…”

Section: Simulation Studiesmentioning

confidence: 99%

“…A rare process called mutation also causes changes in the characteristics of living things. Finally, these new children are considered as parents in the next generation and the process of the algorithm is repeated until the end of the implementation of the algorithm [ 12 ]. The concepts of the GA are simple and understandable, but having control parameters and high computation are the main disadvantages of the GA.…”

Section: Introductionmentioning

confidence: 99%

Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization

Dehghani

Trojovský

2021

Sensors

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Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimization Algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.

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“…Here we have present few research works, out of hundreds of research papers,for scheduling independent sequential jobs in HCS platforms. Author in [22] proposed GA based approach known as Pro-GA for offline scheduling of BoTs in multi-core heterogeneous computing systems for minimization of makespan, resource utilization and speedup ratio. Authors in [23] proposed multi-objective hybrid algorithm by combining CS and GA in pipelined manner for independent sequential task scheduling.…”

Section: B Scheduling On Independent Sequential Jobsmentioning

confidence: 99%

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

2019

IJRTE

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Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.

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A Novel Genetic Algorithm Based Scheduling for Multi-core Systems

Cited by 29 publications

References 14 publications

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

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