A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process

Gnatowski, Andrzej; Niżyński, Teodor

doi:10.3390/app11178083

Cited by 2 publications

(3 citation statements)

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“…In such a model of m ∈  slaves, the simultaneous evaluation of m individuals is possible, which leads to a significant reduction in the total evaluation time of the population. The parallel software implementation will be more meaningful in large-scale optimization problems [33][34][35][36][37][38][39][40].…”

Section: Parallel Master-slave Modelmentioning

confidence: 99%

“…Over the past decades, there has been a growing interest in the parallelization of metaheuristics algorithms [33][34][35][36][37][38][39][40]. Such advanced mechanisms for computation accelerating and enhancement greatly contribute to the success of metaheuristics for solving hard and large-scale optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…An up-to-date review of methods and key contributions to such a research field are described. Other various techniques and strategies of metaheuristics parallelization are described and discussed in [39,40].…”

Section: Introductionmentioning

confidence: 99%

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Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

Jarray

Al‐Dhaifallah

Rezk

2022

Sensors

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The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.

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