Binary Encoding Differential Evolution with Application to Combinatorial Optimization Problem

2013 ◽  
pp. 77-84
Author(s):  
Changshou Deng ◽  
Bingyan Zhao ◽  
Yanlin Yang ◽  
Hai Zhang
Keyword(s):  
Combinatorial Optimization ◽  
Differential Evolution ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Binary Encoding

A Fast Restoration Strategy in Distribution Systems Using an Enhanced Differential Evolution Approach

2010 ◽  
Vol 11 (3) ◽  
Author(s):  
Chao-Ming Huang ◽  
Cheng-Tao Hsieh ◽  
Yann-Chang Huang ◽  
Yung-Shan Wang
Keyword(s):  
Combinatorial Optimization ◽  
Differential Evolution ◽  
Distribution System ◽  
Distribution Systems ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Convergence Time ◽  
Ant System ◽  
Power Company ◽  
Typical Distribution

This paper proposes a fast restoration strategy of distribution systems using an enhanced differential evolution (EDE) approach. Service restoration of distribution systems is an emergent task that must be performed rapidly by the system operators. Basically, it is a complicated combinatorial optimization problem, often having many candidate solutions to be evaluated by the operators. To improve the efficiency of restoration and reduce the burden on the operators, this paper proposes an EDE method combining variable scaling differential evolution (VSDE) algorithm and ant system (AS) to solve the combinatorial optimization problem. To verify the effectiveness of the proposed method, a typical distribution system of the Taiwan Power Company (TPC) was tested and compared with the existing methods. The results show the proposed method was superior to the existing methods in terms of convergence time and the obtained restoration plan.

AN EFFICIENT METHOD FOR NEAR-OPTIMAL POLYGONAL APPROXIMATION BASED ON DIFFERENTIAL EVOLUTION

2008 ◽  
Vol 22 (06) ◽  
pp. 1267-1281 ◽  
Author(s):  
BISWAJIT SARKAR
Keyword(s):  
Combinatorial Optimization ◽  
Differential Evolution ◽  
Efficient Method ◽  
Optimization Problem ◽  
Shape Representation ◽  
Combinatorial Optimization Problem ◽  
Polygonal Approximation

Polygonal approximation of digitized curves is an important step in shape representation and analysis. One of the approaches to obtain a good polygonal approximation is that of formulating the approximation task as an optimization problem and then solving it using the available tools. Adopting this strategy, in this article, we present a differential evolution-based method for polygonal approximation. We show that our method not only yields near-optimal results, but also does so very efficiently. The proposed method is also a demonstration of the successful application of differential evolution for solving a combinatorial optimization problem.

Inverse version of the kth maximization combinatorial optimization problem

2018 ◽  
Vol 54(5) ◽  
pp. 72
Author(s):  
Quoc, H.D. ◽  
Kien, N.T. ◽  
Thuy, T.T.C. ◽  
Hai, L.H. ◽  
Thanh, V.N.
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem

scholarly journals ANALYSIS AND SYNTHESIS OF ENHANCED ANT COLONY OPTIMIZATION WITH THE TRADITIONAL ANT COLONY OPTIMIZATION TO SOLVE TRAVELLING SALES PERSON PROBLEM

2011 ◽  
Vol 1 (1) ◽  
pp. 88-92
Author(s):  
Pallavi Arora ◽  
Harjeet Kaur ◽  
Prateek Agrawal
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Traditional Approach ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Analysis And Synthesis ◽  
Heuristic Technique ◽  
Travelling Salesperson Problem

Ant Colony optimization is a heuristic technique which has been applied to a number of combinatorial optimization problem and is based on the foraging behavior of the ants. Travelling Salesperson problem is a combinatorial optimization problem which requires that each city should be visited once. In this research paper we use the K means clustering technique and Enhanced Ant Colony Optimization algorithm to solve the TSP problem. We show a comparison of the traditional approach with the proposed approach. The simulated results show that the proposed algorithm is better compared to the traditional approach.

Ant Colony Optimization and Multiple Knapsack Problem

2007 ◽  
pp. 498-509 ◽  
Author(s):  
S. Fidanova
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Multiple Knapsack Problem ◽  
Constraint Problem ◽  
Multiple Knapsack ◽  
Ant Colony Optimization Algorithms

The ant colony optimization algorithms and their applications on the multiple knapsack problem (MKP) are introduced. The MKP is a hard combinatorial optimization problem with wide application. Problems from different industrial fields can be interpreted as a knapsack problem including financial and other management. The MKP is represented by a graph, and solutions are represented by paths through the graph. Two pheromone models are compared: pheromone on nodes and pheromone on arcs of the graph. The MKP is a constraint problem which provides possibilities to use varied heuristic information. The purpose of the chapter is to compare a variety of heuristic and pheromone models and different variants of ACO algorithms on MKP.

Sign in / Sign up

Export Citation Format

Share Document