scholarly journals A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252293
Author(s):  
Luda Zhao ◽  
Bin Wang ◽  
Congyong Shen
Keyword(s):  
Fuzzy Number ◽  
Large Scale ◽  
Bat Algorithm ◽  
Triangular Fuzzy Number ◽  
Performance Criteria ◽  
Number Representation ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Time Scheduling

In modern warfare, the comprehensiveness of combat domain and the complexity of tasks pose great challenges to operational coordination.To address this challenge, we use the improved triangular fuzzy number to express the combat mission time, first present a new multi-objective operational cooperative time scheduling model that takes the fluctuation of combat coordinative time and the time flexibility between each task into account. The resulting model is essentially a large-scale multi-objective combinatorial optimization problem, intractably complicated to solve optimally. We next propose multi-objective improved Bat algorithm based on angle decomposition (MOIBA/AD) to quickly identify high-quality solutions to the model. Our proposed algorithm improves the decomposition strategy by replacing the planar space with the angle space, which helps greatly reduce the difficulty of processing evolutionary individuals and hence the time complexity of the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Moreover, the population replacement strategy is enhanced utilizing the improved bat algorithm, which helps evolutionary individuals avoid getting trapped in local optima. Computational experiments on multi-objective operational cooperative time scheduling (MOOCTS) problems of different scales demonstrate the superiority of our proposed method over four state-of-the-art multi-objective evolutionary algorithms (MOEAs), including multi-objective bat Algorithm (MOBA), MOEA/D, non-dominated sorting genetic algorithm version II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO). Our proposed method performs better in terms of four performance criteria, producing solutions of higher quality while keeping a better distribution of the Pareto solution set.

scholarly journals Long-Term Hydropower Generation Scheduling of Large-Scale Cascade Reservoirs Using Chaotic Adaptive Multi-Objective Bat Algorithm

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2373 ◽  
Author(s):  
Lyuwen Su ◽  
Kan Yang ◽  
Hu Hu ◽  
Zhe Yang
Keyword(s):  
Large Scale ◽  
Southern China ◽  
Bat Algorithm ◽  
Economic Benefits ◽  
Population Diversity ◽  
Nsga Ii ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Logical Mapping ◽  
Hydropower Reservoirs

With growing concerns over renewable energy, the cascade hydropower reservoirs operation (CHRO), which balances the development of economic benefits and power supply security, plays an increasingly important role in hydropower systems. Due to conflicting objectives and complicated operation constraints, the CHRO problem considering the requirements of maximizing power generation benefit and firm power output is determined as a multi-objective optimization problem (MOP). In this paper, a chaotic adaptive multi-objective bat algorithm (CAMOBA) is proposed to solve the CHRO problem, and the external archive set is added to preserve non-dominant solutions. Meanwhile, population initialization based on the improved logical mapping function is adopted to improve population diversity. Furthermore, the self-adaptive local search strategy and mutation operation are designed to escape local minima. The CAMOBA is applied to the CHRO problem of the Qingjiang cascade hydropower stations in southern China. The results show that CAMOBA outperforms the multi-objective bat algorithm (MOBA) and non-dominated sorting genetic algorithms-II (NSGA-II) in different hydrological years. The spacing (SP) and hypervolume (HV) metrics verify the excellent performance of CAMOBA in diversity and convergence. In summary, the CAMOBA is demonstrated to get better scheduling solutions, providing an effective approach for solving the cascade hydropower reservoirs operation (CHRO).

scholarly journals A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 244 ◽  
Author(s):  
Chunling Ye ◽  
Zhengyan Mao ◽  
Mandan Liu
Keyword(s):  
Optimization Algorithm ◽  
Test Suite ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Chinese Traditional Culture ◽  
Knee Point ◽  
Five Elements ◽  
Fish Group ◽  
The Individual

Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters’ comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al’s Test suite (ZDT), Deb et al’s Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms.

Study of the Non-Dominated Sorting Genetic Algorithm for the Optimal Structural Parameter of the Tooth-Arrangement Multi-Fingered Dextrous Hand

2013 ◽  
Vol 756-759 ◽  
pp. 4082-4089
Author(s):  
Zhan Li Li ◽  
Xiang Ting He
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Experimental Results ◽  
Structural Parameter ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Multi Objective ◽  
Dextrous Hand

Firstly, the structural parameter optimization of the tooth-arrangement multi-fingered dextrous hand is studied. Secondly, as to the shortcomings that the Pareto solution of multi-objective optimization was distributed unevenly in NSGA-II, a non-dominated sorting genetic algorithm based on immune principle is proposed. Lastly, the structural parameter of the medical tooth-arrangement multi-fingered dextrous hand is optimized using the proposed algorithm. The experimental results show that this algorithm can optimize structural parameter of tooth-arrangement multi-fingered dextrous hand very well.

scholarly journals Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

2021 ◽  
Vol 13 (13) ◽  
pp. 2611
Author(s):  
Huaiyu Qin ◽  
Buhui Zhao ◽  
Leijun Xu ◽  
Xue Bai
Keyword(s):  
Time Delay ◽  
Petri Net ◽  
Information Transfer ◽  
Large Scale ◽  
Information Collection ◽  
Multi Objective Optimization ◽  
Wireless Power ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Uav

Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.

Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

2013 ◽  
Vol 756-759 ◽  
pp. 3136-3140
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Shao Lian Ma
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Approximation Model ◽  
Horizontal Movement ◽  
Nsga Ii ◽  
Hydrodynamic Coefficient ◽  
Pareto Solution ◽  
Multi Objective ◽  
Minimum Drag ◽  
Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

Research on Algorithm of Distributed Reactive Power Optimization Based on Cloud Computing and Improved NSGA-II

2014 ◽  
Vol 644-650 ◽  
pp. 1927-1930
Author(s):  
Bao Yi Wang ◽  
Hao Yin ◽  
Shao Min Zhang
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Cloud Model ◽  
Local Optimum ◽  
Reactive Power Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Crossover And Mutation

A distributed reactive power optimization algorithm is put forward based on cloud computing and improved NSGA-II (fast non-dominated sorting genetic algorithm) in this paper. It is designed to solve problem of multi-objective reactive power optimization with huge amounts of data in power grid, whose difficulties lie in local optimum and slow processing speed. First, NSGA-II's crossover and mutation operator are improved based on Cloud Model, so as to satisfy the adaptive characteristics. In this way, we improved global optimization ability and convergence speed when dealing with large-scale reactive power optimization. Second, we introduced cloud computing, parallelized the proposed algorithm based on MapReduce programming framework. In this way, we achieved distributed improved NSGA-II algorithm, effectively improved the calculation speed of handling massive high-dimensional reactive power optimization. Through theoretical study demonstrated the superiority of the algorithm to solve the Multi-Objective reactive power optimization.

scholarly journals Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment

2021 ◽  
Vol 12 (3) ◽  
pp. 249-272 ◽  
Author(s):  
Nima Farmand ◽  
Hamid Zarei ◽  
Morteza Rasti-Barzoki
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Parallel Machines ◽  
Heuristic Algorithms ◽  
Small Scale ◽  
Scheduling Problem ◽  
Identical Parallel Machines ◽  
Nsga Ii ◽  
Multi Objective ◽  
Large Scale Problems

Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.

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