A Comparison Between Genetic Algorithm and Cuckoo Search Algorithm to Minimize the Makespan for Grid Job Scheduling

Ghosh, T. K.; Das, Sanjoy; Barman, Subhabrata; Goswami, Rajmohan

doi:10.1007/978-981-10-2525-9_14

Cited by 7 publications

(4 citation statements)

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“…The three idealized rules of Cuckoo search and how these rules help in solving the optimization problem are listed in Table 2. Recent studies show that in comparison with Genetic Algorithm, Cuckoo Search has a high exploration ability due to its mutation related Levy flights, yet can converge quickly for job scheduling at grids as in [34].…”

Section: Overview Of Cuckoo Search Algorithm (Csa)mentioning

confidence: 99%

Cuckoo search optimization-based energy efficient job scheduling approach for IoT-edge environment

2023

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Section: Overview Of Cuckoo Search Algorithm (Csa)mentioning

confidence: 99%

Cuckoo search optimization-based energy efficient job scheduling approach for IoT-edge environment

2023

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“…The levy flight concept enables CS to find new solutions with a structure random walk [41]. It has been proved in literature that structured random walks are better in finding a global solution than random walks proposed in other heuristics such as PSO and ant colony optimization [33]. This heuristic has been applied with standard [47] and modified levy flight [48] to solve large scale problems.…”

Section: Cuckoo Searchmentioning

confidence: 99%

“…The leading factor of variance is its stochastic search nature. In comparison, CS is a relatively new heuristic introduced in 2010 and has been reported to outperform GA for various scheduling problems and benchmarks [33]. However, there was a gap in application of CS for constrained and discrete problem of process planning.…”

Section: Introductionmentioning

confidence: 99%

Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment

Khan

Ghafoor

Zahid³

2021

IEEE Access

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The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.

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“…Nature-inspired metaheuristics have established a high degree of success in solving optimization problems with large scale search space [ 18 , 19 ]. Several genetic algorithm (GA) methods have been introduced for grid job scheduling problems [ 20 , 21 , 22 ]. To improve the genetic algorithm further, a hybrid job clustering combining fuzzy C-Mean and a genetic algorithm was developed in [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

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A Comparison Between Genetic Algorithm and Cuckoo Search Algorithm to Minimize the Makespan for Grid Job Scheduling

Cited by 7 publications

References 1 publication

Cuckoo search optimization-based energy efficient job scheduling approach for IoT-edge environment

Cuckoo search optimization-based energy efficient job scheduling approach for IoT-edge environment

Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

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