scholarly journals An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
You Li ◽  
Yingxin Kou ◽  
Zhanwu Li
Keyword(s):  
Reference Points ◽  
Sorting Algorithm ◽  
Nsga Ii ◽  
Target Assignment ◽  
Objective Model ◽  
Typical Instance ◽  
Attack And Defense ◽  
Battlefield Environment ◽  
The Cost ◽  
Nondominated Sorting

Multiobjective weapon-target assignment is a type of NP-complete problem, and the reasonable assignment of weapons is beneficial to attack and defense. In order to simulate a real battlefield environment, we introduce a new objective—the value of fighter combat on the basis of the original two-objective model. The new three-objective model includes maximizing the expected damage of the enemy, minimizing the cost of missiles, and maximizing the value of fighter combat. To solve the problem with complex constraints, an improved nondominated sorting algorithm III is proposed in this paper. In the proposed algorithm, a series of reference points with good performances in convergence and distribution are continuously generated according to the current population to guide the evolution; otherwise, useless reference points are eliminated. Moreover, an online operator selection mechanism is incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving the problem. Finally, the proposed algorithm is applied to a typical instance and compared with other algorithms to verify its feasibility and effectiveness. Simulation results show that the proposed algorithm is successfully applied to the multiobjective weapon-target assignment problem, which effectively improves the performance of the traditional NSGA-III and can produce better solutions than the two multiobjective optimization algorithms NSGA-II and MPACO.

scholarly journals Earthquake Design of Reinforced Concrete Buildings Using NSGA-II

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Herian A. Leyva ◽  
Edén Bojórquez ◽  
Juan Bojórquez ◽  
Alfredo Reyes-Salazar ◽  
José H. Castorena ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Reinforced Concrete ◽  
Seismic Design ◽  
Engineering Practice ◽  
Objective Functions ◽  
Rc Buildings ◽  
Nsga Ii ◽  
Earthquake Design ◽  
The Cost ◽  
Nondominated Sorting

In the present study, the optimal seismic design of reinforced concrete (RC) buildings is obtained. For this purpose, genetic algorithms (GAs) are used through the technique NSGA-II (Nondominated Sorting Genetic Algorithm), thus a multiobjective procedure with two objective functions is established. The first objective function is the control of maximum interstory drift which is the most common parameter used in seismic design codes, while the second is to minimize the cost of the structure. For this aim, several RC buildings are designed in accordance with the Mexico City Building Code (MCBC). It is assumed that the structures are constituted by rectangular and square concrete sections for the beams, columns, and slabs which are represented by a binary codification. In conclusion, this study provides complete designed RC buildings which also can be used directly in the structural and civil engineering practice by means of genetic algorithms. Moreover, genetic algorithms are able to find the most adequate structures in terms of seismic performance and economy.

scholarly journals A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
You Li ◽  
Yingxin Kou ◽  
Zhanwu Li ◽  
An Xu ◽  
Yizhe Chang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Optimization Approach ◽  
Data Simulation ◽  
Nsga Ii ◽  
Mutation Strategy ◽  
Target Assignment ◽  
Operation Factor ◽  
The Cost

The weapon-target assignment (WTA) problem, known as an NP-complete problem, aims at seeking a proper assignment of weapons to targets. The biobjective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles is designed in this paper. A modified Pareto ant colony optimization (MPACO) algorithm is used to solve the BOWTA problem. In order to avoid defects in traditional optimization algorithms and obtain a set of Pareto solutions efficiently, MPACO algorithm based on new designed operators is proposed, including a dynamic heuristic information calculation approach, an improved movement probability rule, a dynamic evaporation rate strategy, a global updating rule of pheromone, and a boundary symmetric mutation strategy. In order to simulate real air combat, the pilot operation factor is introduced into the BOWTA model. Finally, we apply the MPACO algorithm and other algorithms to the model and compare the data. Simulation results show that the proposed algorithm is successfully applied in the field of WTA which improves the performance of the traditional P-ACO algorithm effectively and produces better solutions than the two well-known multiobjective optimization algorithms NSGA-II and SPEA-II.

An Efficient Non-dominated Sorting Method for Evolutionary Algorithms

2008 ◽  
Vol 16 (3) ◽  
pp. 355-384 ◽  
Author(s):  
Hongbing Fang ◽  
Qian Wang ◽  
Yi-Cheng Tu ◽  
Mark F. Horstemeyer
Keyword(s):  
Evolutionary Algorithms ◽  
Divide And Conquer ◽  
Computational Time ◽  
Sorting Algorithm ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Overall Efficiency ◽  
Dominance Tree ◽  
The Cost

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN2) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

An Improved Nondominated Sorting Algorithm

2012 ◽  
Vol 3 (4) ◽  
pp. 20-42
Author(s):  
André R. da Cruz
Keyword(s):  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Computational Cost ◽  
Sorting Algorithm ◽  
Nsga Ii ◽  
Worst Case ◽  
New Strategy ◽  
Nondominated Sorting ◽  
Correct Level ◽  
Better Than

This paper presents a new procedure for the nondominated sorting with constraint handling to be used in a multiobjective evolutionary algorithm. The strategy uses a sorting algorithm and binary search to classify the solutions in the correct level of the Pareto front. In a problem with objective functions, using solutions in the population, the original nondominated sorting algorithm, used by NSGA-II, has always a computational cost of in a naïve implementation. The complexity of the new algorithm can vary from in the best case and in the worst case. A experiment was executed in order to compare the new algorithm with the original and another improved version of the Deb’s algorithm. Results reveal that the new strategy is much better than other versions when there are many levels in Pareto front. It is also concluded that is interesting to alternate the new algorithm and the improved Deb’s version during the evolution of the evolutionary algorithm.

scholarly journals Optimization of Wheel Reprofiling Based on the Improved NSGA-II

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xinghu Wang ◽  
Jiabin Yuan ◽  
Sha Hua ◽  
Bojia Duan
Keyword(s):  
Genetic Operators ◽  
Wheel Wear ◽  
Nsga Ii ◽  
Attrition Rates ◽  
Decision Variables ◽  
Flange Thickness ◽  
Wheel Model ◽  
The Cost ◽  
The Relationship ◽  
Nondominated Sorting

Wheels are the key components of a train, and the shape of the wheel flange should be maintained to ensure the security of train operations. As a method to maintain the shape at the cost of the diameter size, reprofiling has significant impacts on the lifecycle of a train. A wheel model is built in this paper based on the analysis of the wheel wear features and datasets from Taiyuan locomotives. With the decision variables T i , T i ′ , which describe the reprofiling strategy, we formulate a multiobjective optimization problem simultaneously minimizing the reprofiling numbers and maximizing the serving years. To find the solutions of the multiobjective model, the NSGA-II (nondominated sorting genetic algorithm II) is extended with an alteration of the crowding distance calculation and genetic operators. The improved NSGA-II performs better than other approaches (e.g., fixed reprofiling strategy, changeable reprofiling strategy, and NSGA-II). Meanwhile, outstanding solutions with longer servicing years and less reprofiling are listed in this paper. Our study reveals the relationship between the diameter, flange thickness, and their individual attrition rates and proposes a wear model, multiobjective model, and improved NSGA-II. Compared with existing reprofiling strategies, the strategy recommended in our work can significantly increase the lifecycle of the wheel coupled with a low repair frequency.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

2022 ◽  
Vol 204 ◽  
pp. 111999
Author(s):  
Hanting Wu ◽  
Yangrui Huang ◽  
Lei Chen ◽  
Yingjie Zhu ◽  
Huaizheng Li
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Nsga Ii ◽  
Sewer Pipes ◽  
Nondominated Sorting

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

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