Chou-Yuan Lee

An immunity-based ant colony optimization algorithm for solving weapon–target assignment problem

Abstract In this paper, an immunity-based ant colony optimization (ACO) algorithm for solving weapon–target assignment (WTA) problems is proposed. The WTA problem, known as a NP-complete problem, is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force assets. The general idea of the proposed algorithm is to combine the advantages of ACO, the ability to cooperatively explore the search space and to avoid premature convergence, and that of immune system (IS), the ability to quickly find good solutions within a small region of the search space. From our simulation for those WTA problems, the proposed algorithm indeed is very efficient.

Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics

A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of offspring. The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring. This algorithm is successfully applied to general WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms.

A hybrid search algorithm with heuristics for resource allocation problem

The resource allocation problelm is to allocate resources to activities so that the cost becomes as optimal as possible. In this paper, a hybrid search algorithm with heuristics for resource allocation problem encountered in practice is proposed. The proposed algorithm has both the advantages of genetic algorithm (GA) and ant colony optimization (ACO) that can explore the search space and exploit the best solution. In our implelmentation, both GA and ACO are well designed for the resource allocation problelm. Fur thermore, heuristics are used to ameliorate the search performance for resource allocation problem. Simulation results were reported and the proposed algorithm indeed have admirable performance for tested problems.

Applying hybrid meta-heuristics for capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP) is one of the most important problems in the optimization of distribution networks. The objective of CVRP, known demands on the cost of originating and terminating at a delivery depot, is to determine the optimal set of routes for a set of vehicles to deliver customers. CVRP is known to be NP-hard problem, and then it is difficult to solve this problem directly when the problem size is large. In this paper, a hybrid algorithm of simulated annealing and tabu search is applied to solve CVRP. It takes the advantages of simulated annealing and tabu search for solving CVRP. Simulation results are reported on classical fourteen instances and twenty large-scale benchmark instances. From simulation results, the proposed algorithm finds eight best solutions of classical fourteen instances. Additionally, the solutions of the proposed algorithm have also admirable performance for twenty large-scale benchmark instances. It shows that the proposed algorithm is competitive with other existing algorithms for solving CVRP.

A GENETIC ALGORITHM WITH DOMAIN KNOWLEDGE FOR WEAPON-TARGET ASSIGNMENT PROBLEMS

Abstract In this paper, a novel genetic algorithm, including domain specific knowledge into the crossover operator and the local search mechanism for solving weapon‐target assignment (WTA) problems is proposed. The WTA problem is a full assignment of weapons to hostile targets with the objective of minimizing the expected damage value to own‐force assets. It is an NP‐complete problem. In our study, a greedy reformation and a new crossover operator are proposed to improve the search efficiency. The proposed algorithm outperforms its competitors on all test problems.

An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem

In this paper, an enhanced ant colony optimization (EACO) is proposed for capacitated vehicle routing problem. The capacitated vehicle routing problem is to service customers with known demands by a homogeneous fleet of fixed capacity vehicles starting from a depot. It plays a major role in the field of logistics and belongs to NP-hard problems. Therefore, it is difficult to solve the capacitated vehicle routing problem directly when solutions increase exponentially with the number of serviced customers.The framework of this paper is to develop an enhanced ant colony optimization for the capacitated vehicle routing problem. It takes the advantages of simulated annealing and ant colony optimization for solving the capacitated vehicle routing problem. In the proposed algorithm, simulated annealing provides a good initial solution for ant colony optimization. Furthermore, an information gain based ant colony optimization is used to ameliorate the search performance. Computational results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on fourteen small-scale instances and twenty large-scale instances.

A Heuristic Genetic Algorithm for Solving Resource Allocation Problems

Abstract.In the paper, a heuristic genetic algorithm for solving resource allocation problems is proposed. The resource allocation problems are to allocate resources to activities so that the fitness becomes as optimal as possible. The objective of this paper is to develop an efficient algorithm to solve resource allocation problems encountered in practice. Various genetic algorithms are studied and a heuristic genetic algorithm is proposed to ameliorate the rate of convergence for resource allocation problems. Simulation results show that the proposed algorithm gives the best performance.

A New Approach for Solving 0/1 Knapsack Problem

In this paper, we reported our study on solving 0/1 knapsack problem effectively by using ant colony optimization. The 0/1 knapsack problem is to maximize the total profit under the constraint that the total weight of all chosen objects is at the most weight limit. In our study, we viewed the search in ant colonies as a mechanism providing a better performance and it has the ability to escape from local optima. In this paper, several examples are tested to demonstrate the superiority of the proposed algorithm. From simulation results, the proposed algorithm indeed has remarkable performance.

A novel algorithm applied to classify unbalanced data

Unbalanced data that are minority classes with few samples presented in many fields. The mean of unbalanced data is difficult to formalize so that traditional algorithms are limited in solving unbalanced data. In this paper, a novel algorithm based on analysis of variance (ANOVA), fuzzy C-means (FCM) and bacterial foraging optimization (BFO) is proposed to classify unbalanced data. ANOVA can measure the difference between the means of two or more groups in which the observed variance is partitioned into components due to various explanatory variables. FCM is a method of fuzzy clustering algorithm that allows one piece of data to belong to two or more clusters. Natural selection tends to eliminate animals with poor foraging strategies and favors the propagation of genes of those animals that have successful foraging strategies. BFO can model the mechanism of natural selection and solve many application problems. The proposed algorithm combines the advantages of ANOVA, FCM and BFO. ANOVA has the ability to select beneficial feature subsets. FCM has the ability to identify data into clusters with certain membership degrees, and BFO has the fast ability to converge to global optima. In this paper, microarray data of ovarian cancer and zoo dataset are used to test the performance for the proposed algorithm. The performance of the proposed algorithm is supported by simulation results. From simulation results, the classification accuracy of the proposed algorithm outperforms other existing approaches.

Incorporating Hopfield Neural Networks into Ant Colony System for Traveling Salesman Problem

In this paper, the approach of incorporating Hopfield neural networks (HNN) into ant colony systems (ACS) is proposed and studied. In the proposed approach (HNNACS), HNN is used to find a plausibly good solution, which is then used in ACS as the currently best tour for the offline pheromone trail update. The idea is to deposit additional pheromone to ACS to enhance the search efficiency. From simulation results, the search efficiency of HNNACS is better than other existing algorithms.