Solution for flow shop scheduling problems using chaotic hybrid firefly and particle swarm optimization algorithm with improved local search

Kaya, Serkan; Gümüşçü, Abdülkadir; Aydilek, İbrahim Berkan; Karaçizmeli, İzzettin Hakan; Tenekeci, Mehmet Emin

doi:10.1007/s00500-021-05673-w

Cited by 27 publications

(14 citation statements)

References 73 publications

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“…Kaya and others introduced the fuzzy control theory into mobile robot path planning and proposed a robot path planning method based on multiple motion instruction sets. This method uses fuzzy rules to guide the motion of the robot, avoids the disadvantage of poor real-time performance of mobile robot path planning due to large amount of calculation, and achieved good simulation results [14]. Pahnehkolaei and others combined the improved genetic algorithm with the Dijkstra algorithm (DA) to optimize the robot path and designed a heuristic way to generate the initial population.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Fan

2022

Journal of Sensors

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In order to improve the efficiency of a logistics cycle robot picking up goods, a path planning algorithm based on artificial intelligence was proposed. After analyzing the particle swarm optimization algorithm, the particle swarm optimization algorithm is optimized and improved and the path planning of a single robot is obtained. On this basis, a multipopulation particle swarm optimization (CMMPPSO) algorithm is proposed. The results show that the JMPOPSO algorithm is more accurate than the BPSO algorithm and the maximum fitness optimized by the BPSO algorithm is 1.59, while the maximum fitness optimized by the JMPOPSO algorithm is 1.98. The path optimized by the CMMPPSO algorithm based on JMPOPSO is better than that optimized by the CMMPPSO algorithm based on BPSO, shortening by about 25% and shortening the time by about 30. Simulation experiments verify the effectiveness of the CMMPPSO algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Fan

2022

Journal of Sensors

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“…(5) Calculate the fitness value of t 1 . If it is better than the previous generation, the next generation particle swarm t 2 will be generated; otherwise, it will be converted to Cartesian coordinates [20]. (6) e algorithm is over.…”

Section: Improve Pso Path Planning Algorithm Flowmentioning

confidence: 99%

“…Particle swarm optimization algorithm with dynamic nonlinear inertial weights was used for robot path planning (static obstacles) [21]. Simulation parameters are starting point [5,5], target point [25,25], polar radius � 5, obstacle point [20,20], [8,10], [10,10], [12,10], [24,20], [18,20]; learning factor c 1 � c 2 � 1.4962; inertia weight w max � 0.9; w min �0.4; dimension of search space m 10; population number N 30, iteration number T, maximum iteration number DT max 2000;…”

Section: Static Obstacle Avoidance With Particle Swarmmentioning

confidence: 99%

“…As can be seen in Figure 3, when T 5, the football robot starts to avoid the obstacle point [10,10]. When T 15, the football robot starts to avoid the obstacle point [20,20]. e inertia weight changes according to the linear decreasing law, and the path planned by PSO can avoid obstacles, but the e ect is not very obvious [22].…”

Section: Static Obstacle Avoidance With Particle Swarmmentioning

confidence: 99%

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Trajectory Tracking of Soccer Motion Based on Multiobject Detection Algorithm

Xie¹

2022

Mathematical Problems in Engineering

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In order to improve the tracking ability of soccer robot in the complicated static and dynamic environment, a method of soccer trajectory tracking based on multiobject detection algorithm is proposed. This method makes use of the advantages of convenient polar coordinate calculation and realistic path simulation and puts the path coding of the soccer robot in two-dimensional polar coordinates. Then, the distance relationship between the current path point, the next path point, and the obstacle point is used to judge whether to carry out path planning. When the obstacle is encountered, the improved PSO algorithm with nonlinear inertia weight is called to carry out path planning. The simulation results show that under two obstacles, when the number of iterations is T = 19 , the soccer robot starts to avoid obstacles and intercept. When iteration number T = 10 , the soccer robot starts to avoid obstacles and intercept side by side. Under multiple obstacles, when T = 19 , the soccer robot starts to avoid obstacles and intercept in front. When T = 30 , the soccer robot reaches the target point. The convergence of the improved PSO algorithm and the effectiveness of path planning are verified.

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“…As the inertia weight of particle swarm increases, the planning ability of the algorithm will be enhanced. As the inertia weight of particle swarm decreases, the algorithm will only have local planning ability [13]. Generally, the optimal range of particle swarm inertia weight parameter is 0 ∼ 1.4, but the convergence speed of the algorithm will be accelerated when the value of particle swarm inertia weight parameter is 0.8 ∼ 1.2.…”

Section: Selecting Particle Swarm Optimizationmentioning

confidence: 99%

Urban Rail Transit Network Planning Based on Particle Swarm Optimization Algorithm

2022

Mathematical Problems in Engineering

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In order to solve the problem that the urban rail transit network is affected by a large number of signals, resulting in poor control effect, and improve the living comfort of residents near urban rail transit, a study on urban rail transit network planning based on particle swarm optimization algorithm is proposed. The learning factor is dynamically adjusted according to the inertia weight parameters, and the particle swarm optimization parameters are selected in combination with the setting of the maximum velocity parameters. The individual optimal particle is selected by using the dominant relationship between the individual particles, and the optimization of the optimal particle is completed by combining the selection requirements of the global optimal particle. We design the v2x communication implementation scheme, obtain the traffic flow information of urban rail transit, build the signal input and output model based on particle swarm optimization algorithm, obtain the output feedback signal, and determine the planning scale of urban rail transit network, so as to build the urban rail transit network planning model and complete the urban rail transit network planning. The experimental results show that the proposed method can improve the utilization rate of urban rail transit network planning, effectively control the change of network signal amplitude, and reduce the repetition rate of urban rail transit network planning.

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Solution for flow shop scheduling problems using chaotic hybrid firefly and particle swarm optimization algorithm with improved local search

Cited by 27 publications

References 73 publications

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Research on Intelligent Pick-Up Route Planning of a Logistics Cycle Automatic Robot

Trajectory Tracking of Soccer Motion Based on Multiobject Detection Algorithm

Urban Rail Transit Network Planning Based on Particle Swarm Optimization Algorithm

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