scholarly journals A Hybrid Discrete Grey Wolf Optimizer to Solve Weapon Target Assignment Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Jun Wang ◽  
Pengcheng Luo ◽  
Xinwu Hu ◽  
Xiaonan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Solution Space ◽  
Grey Wolf Optimizer ◽  
Local Search Algorithm ◽  
Grey Wolf ◽  
Target Assignment ◽  
Encoding Method

We propose a hybrid discrete grey wolf optimizer (HDGWO) in this paper to solve the weapon target assignment (WTA) problem, a kind of nonlinear integer programming problems. To make the original grey wolf optimizer (GWO), which was only developed for problems with a continuous solution space, available in the context, we first modify it by adopting a decimal integer encoding method to represent solutions (wolves) and presenting a modular position update method to update solutions in the discrete solution space. By this means, we acquire a discrete grey wolf optimizer (DGWO) and then through combining it with a local search algorithm (LSA), we obtain the HDGWO. Moreover, we also introduce specific domain knowledge into both the encoding method and the local search algorithm to compress the feasible solution space. Finally, we examine the feasibility of the HDGWO and the scalability of the HDGWO, respectively, by adopting it to solve a benchmark case and ten large-scale WTA problems. All of the running results are compared with those of a discrete particle swarm optimization (DPSO), a genetic algorithm with greedy eugenics (GAWGE), and an adaptive immune genetic algorithm (AIGA). The detailed analysis proves the feasibility of the HDGWO in solving the benchmark case and demonstrates its scalability in solving large-scale WTA problems.

scholarly journals A Matheuristic Approach Combining Local Search and Mathematical Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Carolina Lagos ◽  
Guillermo Guerrero ◽  
Enrique Cabrera ◽  
Stefanie Niklander ◽  
Franklin Johnson ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Facility Location Problem ◽  
Specific Information ◽  
Local Search Algorithm ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location ◽  
Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

scholarly journals Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Lan-Fen Liu ◽  
Xin-Feng Yang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Search Algorithm ◽  
Production Costs ◽  
Total Production ◽  
Local Search Algorithm ◽  
Aggregate Production Planning ◽  
Nsga Ii ◽  
Aggregate Production ◽  
Multi Objective

AbstractThe diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.

scholarly journals An Iterated Hybrid Local Search Algorithm for Pick-and-Place Sequence Optimization

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 633 ◽  
Author(s):  
Jinsheng Gao ◽  
Xiaomin Zhu ◽  
Anbang Liu ◽  
Qingyang Meng ◽  
Runtong Zhang
Keyword(s):  
Integer Programming ◽  
Local Search ◽  
Large Scale ◽  
Programming Model ◽  
Search Algorithm ◽  
Local Search Algorithm ◽  
Pick And Place ◽  
Feeder Assignment ◽  
High Computational Efficiency ◽  
The Greedy Algorithm

This paper shows the results of our study on the pick-and-place optimization problem. To solve this problem efficiently, an iterated hybrid local search algorithm (IHLS) which combines local search with integer programming is proposed. In the section of local search, the greedy algorithm with distance weight strategy and the convex-hull strategy is developed to determine the pick-and-place sequence; in the section of integer programming, an integer programming model is built to complete the feeder assignment problem. The experimental results show that the IHLS algorithm we proposed has high computational efficiency. Furthermore, compared with the genetic algorithm and the memetic algorithm, the IHLS is less time-consuming and more suitable in solving a large-scale problem.

VLSs: A Local Search Algorithm for Distributed Constraint Optimization Problems

2022 ◽  
Author(s):  
Fukui Li ◽  
Jingyuan He ◽  
Mingliang Zhou ◽  
Bin Fang
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Stochastic Algorithm ◽  
Local Search Algorithm ◽  
Constraint Optimization ◽  
Local Optima ◽  
Distributed Constraint Optimization ◽  
Adjustment Mechanism

Local search algorithms are widely applied in solving large-scale distributed constraint optimization problem (DCOP). Distributed stochastic algorithm (DSA) is a typical local search algorithm to solve DCOP. However, DSA has some drawbacks including easily falling into local optima and the unfairness of assignment choice. This paper presents a novel local search algorithm named VLSs to solve the issues. In VLSs, sampling according to the probability corresponding to assignment is introduced to enable each agent to choose other promising values. Besides, each agent alternately performs a greedy choice among multiple parallel solutions to reduce the chance of falling into local optima and a variance adjustment mechanism to guide the search into a relatively good initial solution in a periodic manner. We give the proof of variance adjustment mechanism rationality and theoretical explanation of impact of greed among multiple parallel solutions. The experimental results show the superiority of VLSs over state-of-the-art DCOP algorithms.

scholarly journals A Restart Local Search for Solving Diversified Top-k Weight Clique Search Problem

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2674
Author(s):  
Jun Wu ◽  
Minghao Yin
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Weighted Graph ◽  
Search Problem ◽  
Local Search Algorithm ◽  
Practical Applications ◽  
Maximal Weight ◽  
Restart Strategy ◽  
Constraint Model

Diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with practical applications. The diversified top-k weight clique search problem aims to search k maximal cliques that can cover the maximum weight in a vertex weighted graph. In this work, we propose a novel local search algorithm called TOPKWCLQ for the DTKWC search problem which mainly includes two strategies. First, a restart strategy is adopted, which repeated the construction and updating processes of the maximal weight clique set. Second, a scoring heuristic is designed by giving different priorities for maximal weight cliques in candidate set. Meanwhile, a constraint model of the DTKWC search problem is constructed such that the research concerns can be evaluated. Experimental results show that the proposed algorithm TOPKWCLQ outperforms than the comparison algorithm on large-scale real-world graphs.

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