scholarly journals Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm

2021 ◽  
Vol 9 (4) ◽  
pp. 357
Author(s):  
Wei Zhao ◽  
Yan Wang ◽  
Zhanshuo Zhang ◽  
Hongbo Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fuel Consumption ◽  
Particle Swarm ◽  
Route Planning ◽  
Planning Method ◽  
Pareto Optimal ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ship Route

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

scholarly journals An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Tao Zhang ◽  
Tiesong Hu ◽  
Yue Zheng ◽  
Xuning Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Problem ◽  
Multiobjective Programming ◽  
Particle Swarm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Multiobjective Programming Problem

An improved particle swarm optimization (PSO) algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP). For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

Fast Multilevel Thresholding Method for Image Segmentation Based on Improved Particle Swarm Optimization and Maximal Variance

2012 ◽  
Vol 532-533 ◽  
pp. 1741-1746 ◽  
Author(s):  
Zheng Tao Peng ◽  
Kang Ling Fang ◽  
Zhi Qi Su ◽  
Shi Hong Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Multilevel Thresholding ◽  
Object Function ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Thresholding Method ◽  
Optimal Thresholds

To determine the optimal thresholds in image segmentation, a new multilevel thresholding method based on improved particle swarm optimization (IPSO) is proposed in this paper. Firstly, use the conception of independent peaks to divide the histogram to several regions, secondly, the optimization object function using maximum between-class variance (MV) method can be gotten in each area, by the non-uniform mutation and Geese-LDW PSO optimization of the object function, the optimal thresholds can be gotten, and the image can be segmented with the thresholds. Compared with the basic MV algorithm and genetic algorithm (GA) modified MV, the experimental results show that the new method not only realizes the image segmentation well, but also improves the speed.

scholarly journals A Hyper Heuristics Technique for Data Partitioning and Scheduling to Heterogeneous Systems using Genetic Algorithm and Improved Particle Swarm Optimization

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1696-1701
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Optimal Number ◽  
Data Partitioning ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fitness Value ◽  
Real Coded Genetic Algorithm

Development of the load partitioning for multiple round load distribution and effective scheduling of partitioned load to heterogeneous processor is primary goal of distributed and parallel system. In this paper, we propose hyper heuristics scheduling algorithm for load partitioning using genetic and improved particle swarm optimization techniques.A communication model is used to predict the optimal activation order, optimal number of processor and optimal number of rounds of the load. Heuristics Based Scheduling Algorithm is proposed using Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) to form Scheduling Solutions (heuristics algorithms) for large scale system with diversity operator as sequence dependent and sequence independent scheduling. For this solution, processing time of the entire processing load will be reduced. Hybrid Real Code genetic algorithm(HRGA) computes optimal activation order with cross over and mutation operator without considering the processor latency and different types of variation in the perturbation parameters. In order to optimize this issue, we utilizeImproved Particle swarm optimization (IPSO) determine the load fraction for generating activation order in terms of dynamically predicting fitness value of the processor with certain number. The Simulation analysis demonstrates the proposed model performance in terms of mean, standard deviation, computational complexity and Average Execution Time comparing against hybrid real coded genetic algorithm

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