Ant colony system with a novel Non-DaemonActions procedure for multiprocessor task scheduling in multistage hybrid flow shop

Kurdi, Mohamed Zakaria

doi:10.1016/j.swevo.2018.10.012

Cited by 30 publications

(16 citation statements)

References 65 publications

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“…where ρ is local pheromone evaporation coefficient whose rang is [0, 1]; τ 0 is pheromone initial value. Global pheromone update rule: After all ants completed their tour, only the global optimal path can update the pheromone, which accelerates the convergence of the algorithm, and its expression is formula (5).…”

Section: ) Construct the Solutionmentioning

confidence: 99%

“…Ant colony algorithm has been successfully applied in several fields, the most successful of which is used for combinatorial optimization problems, therefore, the ant colony optimization proposed in this paper adopts the TSP problem for experimental testing. In the future, we will use ant colony optimization to solve the robot path planning and task scheduling problems [5].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

et al. 2019

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Ant Colony Optimization has achieved good results in solving Traveling Salesman Problem (TSP), it has a tendency to fall into local optima and the convergence speed is limited. To address this problem, multi-colony ant colony optimization based on the generalized Jaccard similarity recommendation strategy (JCACO) is proposed. Firstly, two classical ant populations, Ant Colony System and Max-Min Ant System are selected to form heterogeneous multi-colony. Secondly, attribute-based collaborative filtering recommendation mechanism is proposed to balance the diversity and convergence of the algorithm, three strategies have been implemented under this recommendation mechanism: The attribute cross-learning strategy is used to highlight the effect of excellent attributes and improve the attribute comprehensive performance; According to the diversity results of the population measured by information entropy, the attribute recommendation learning strategy is used to enrich the diversity of the population adaptively; The pheromone reward strategy is implemented on the public path to accelerate the convergence speed; Among which, according to the generalized Jaccard similarity coefficient, the most suitable communication object is recommended in order to achieve the best learning efficiency. Finally, when the algorithm stagnates, the elite reverse learning mechanism is used to jump out of the local optimum. Experimental results show that JCACO has good performance and high stability in TSP instances, especially in large-scale TSP instances. INDEX TERMS Attribute cross-learning, attribute recommendation learning, ant colony optimization, elite reverse learning, generalized Jaccard similarity coefficient, traveling salesman problem.

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Section: ) Construct the Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

et al. 2019

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“…However, due to the greedy nature, heuristic-based methods can not always produce consistent results for different problem instances [3], [12]. Because of the high adaptability, many well-known meta-heuristic algorithms have been adopted, including Genetic Algorithms (GA) [12], [14]- [19], and [20], Simulated Annealing algorithms (SA) [21], [22], Quantum-inspired Hyper-heuristics Algorithms (QHA) [3], [16], Ant Colony Optimization (ACO) [23]- [26], etc. However, the search process of the meta-heuristic algorithm varies from problem to problem, and has the disadvantages of large randomness, low global search efficiency, and premature convergence in the late iteration.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

et al. 2020

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As a technique to help achieve high performance in parallel and distributed heterogeneous computing systems, task scheduling has attracted considerable interest. In this paper, we propose an effective Cuckoo Search algorithm based on Gaussian random walk and Adaptive discovery probability which combined with a cost-to-time ratio Modification strategy (GACSM), to address task scheduling on heterogeneous multiprocessor systems using Dynamic Voltage and Frequency Scaling (DVFS). First, to overcome the shortcomings of poor performance in exploitation of the cuckoo search algorithm, we use chaos variables to initialize populations to maintain the population diversity, a Gaussian random walk strategy to balance the exploration and exploitation capabilities of the algorithm, and an adaptive discovery probability strategy to improve population diversity. Then, we apply the improved Cuckoo Search (CS) algorithm to assign tasks to resources, and a widely used downward rank heuristic strategy to find the corresponding scheduling sequence. Finally, we apply a cost-to-time ratio improvement strategy to further improve the performance of the improved CS algorithm. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show its superiority in comparison with the state-of-the-art methods. INDEX TERMS Task scheduling, DVFS, cuckoo search algorithm, heterogeneous multiprocessor system.

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“…The work presented in this paper is to reduce the computational time (CT PJ max ) and makespan of the Taillard dataset. Flow shop scheduling [8] with multiprocessor increase the computational capacity and also reduce the cost of the machine. There are various researchers have proposed several heuristic algorithmssuch as Genetic Algorithm (GA), Tabu Search, Particle Swarm Optimization (PSO) algorithms, etc.to provide the near-optimal solution at the relatively minor computational expense [9].…”

Section: Introductionmentioning

confidence: 99%

A Deterministic Flowshop Scheduling Problem to minimizing the Makespan using PA ACO

Priya,

Sahana

2020

IJEAT

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Scheduling problems are NP-hard in nature. Flowshop scheduling problems, are consist of sets of machines with number of resources. It matins the continuous flow of task with minimum time. There are various traditional algorithms to maintain the order of resources. Here, in this paper a new stochastic Ant Colony optimization technique based on Pareto optimal (PA-ACO) is implemented for solving the permutation flowshop scheduling problem (PFSP) sets. The proposed technique is employed with a novel local path search technique for initializing and pheromone trails. Pareto optimal mechanism is used to select the best optimal path solution form generated solution sets. A comparative study of the results obtained from simulations shows that the proposed PA-ACO provides minimum makespan and computational time for the Taillard dataset. This work will applied on large scale manufacturing production problem for efficient energy utilization.

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Ant colony system with a novel Non-DaemonActions procedure for multiprocessor task scheduling in multistage hybrid flow shop

Cited by 30 publications

References 65 publications

Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

Multi-Colony Ant Colony Optimization Based on Generalized Jaccard Similarity Recommendation Strategy

Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

A Deterministic Flowshop Scheduling Problem to minimizing the Makespan using PA ACO

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