scholarly journals Distance Evaluated Simulated Kalman Filter with State Encoding for Combinatorial Optimization Problems

2018 ◽  
Vol 7 (4.27) ◽  
pp. 22
Author(s):  
Zulkifli Md Yusof ◽  
Zuwairie Ibrahim ◽  
Asrul Adam ◽  
Kamil Zakwan Mohd Azmi ◽  
Tasiransurini Ab Rahman ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Combinatorial Problem ◽  
Search Space ◽  
Population Based ◽  
Routing And Scheduling ◽  
Combinatorial Optimization Problems ◽  
Binary Encoding ◽  
State Encoding

Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance evaluated SKF (DESKF). These three combinatorial SKF algorithms use binary encoding to represent the solution to a combinatorial optimization problem. This paper introduces the latest version of distance evaluated SKF which uses state encoding, instead of binary encoding, to represent the solution to a combinatorial problem. The algorithm proposed in this paper is called state-encoded distance evaluated SKF (SEDESKF) algorithm. Since the original SKF algorithm tends to converge prematurely, the distance is handled differently in this study. To control and exploration and exploitation of the SEDESKF algorithm, the distance is normalized. The performance of the SEDESKF algorithm is compared against the existing combinatorial SKF algorithm based on a set of Traveling Salesman Problem (TSP).      

Space Contraction and Partition Algorithm for Combinatorial Optimization

2011 ◽  
Vol 421 ◽  
pp. 559-563
Author(s):  
Yong Chao Gao ◽  
Li Mei Liu ◽  
Heng Qian ◽  
Ding Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Search Space ◽  
Small Scale ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Intelligent Algorithms ◽  
Combinatorial Optimization Problems

The scale and complexity of search space are important factors deciding the solving difficulty of an optimization problem. The information of solution space may lead searching to optimal solutions. Based on this, an algorithm for combinatorial optimization is proposed. This algorithm makes use of the good solutions found by intelligent algorithms, contracts the search space and partitions it into one or several optimal regions by backbones of combinatorial optimization solutions. And optimization of small-scale problems is carried out in optimal regions. Statistical analysis is not necessary before or through the solving process in this algorithm, and solution information is used to estimate the landscape of search space, which enhances the speed of solving and solution quality. The algorithm breaks a new path for solving combinatorial optimization problems, and the results of experiments also testify its efficiency.

scholarly journals On the Binarization of Grey Wolf Optimizer‎: ‎A Novel Binary Optimizer Algorithm

2021 ◽  
Author(s):  
Mehdy Roayaei
Keyword(s):  
Combinatorial Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Combinatorial Optimization Problems ◽  
Hunting Behaviour ◽  
Set Operations

Abstract ‎Grey Wolf Optimizer (GWO) is a population-based evolutionary algorithm inspired by the hunting behaviour of grey wolves‎. ‎GWO‎, ‎in its basic form‎, ‎is a real coded algorithm‎, ‎therefore‎, ‎it needs modifications to deal with binary optimization problems‎. ‎In this paper‎, ‎we review previous works on binarization of GWO‎, ‎and classify them with respect to their encoding scheme‎, ‎updating strategy‎, ‎and transfer function‎. ‎Then‎, ‎we propose a novel binary GWO algorithm (named SetGWO)‎, ‎which is based on set encoding and uses set operations in its updating strategy‎. ‎Experimental results on different real-world combinatorial optimization problems and different datasets‎, ‎show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions‎, ‎running time‎, ‎and scalability‎.

An Evaluation of Methods for Estimating the Number of Local Optima in Combinatorial Optimization Problems

2013 ◽  
Vol 21 (4) ◽  
pp. 625-658 ◽  
Author(s):  
Leticia Hernando ◽  
Alexander Mendiburu ◽  
Jose A. Lozano
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Space ◽  
Combinatorial Optimization Problem ◽  
Local Search Algorithms ◽  
Neighborhood Structure ◽  
Local Optima ◽  
Combinatorial Optimization Problems ◽  
Evaluation Of Methods

The solution of many combinatorial optimization problems is carried out by metaheuristics, which generally make use of local search algorithms. These algorithms use some kind of neighborhood structure over the search space. The performance of the algorithms strongly depends on the properties that the neighborhood imposes on the search space. One of these properties is the number of local optima. Given an instance of a combinatorial optimization problem and a neighborhood, the estimation of the number of local optima can help not only to measure the complexity of the instance, but also to choose the most convenient neighborhood to solve it. In this paper we review and evaluate several methods to estimate the number of local optima in combinatorial optimization problems. The methods reviewed not only come from the combinatorial optimization literature, but also from the statistical literature. A thorough evaluation in synthetic as well as real problems is given. We conclude by providing recommendations of methods for several scenarios.

Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization

2020 ◽  
Vol 12 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Kenneth Brezinski ◽  
Michael Guevarra ◽  
Ken Ferens
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Population Based ◽  
Convergence Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Benchmark Instances

This article introduces a hybrid algorithm combining simulated annealing (SA) and particle swarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems. The implementation carried out a dynamic determination of the equilibrium loops in SA through a simple, yet effective determination based on the recent performance of the swarm members. In particular, the authors demonstrated that strong improvements in convergence time followed from a marginal decrease in global search efficiency compared to that of SA alone, for several benchmark instances of the traveling salesperson problem (TSP). Following testing on 4 additional city list TSP problems, a 30% decrease in convergence time was achieved. All in all, the hybrid implementation minimized the reliance on parameter tuning of SA, leading to significant improvements to convergence time compared to those obtained with SA alone for the 15 benchmark problems tested.

TCGD: A Time-Constrained Approximate Guided Depth-First Search Algorithm

1997 ◽  
Vol 06 (02) ◽  
pp. 255-271 ◽  
Author(s):  
Benjamin W. Wah ◽  
Lon-Chan Chu
Keyword(s):  
Combinatorial Optimization ◽  
Polynomial Time ◽  
Execution Time ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Fixed Time ◽  
Depth First Search ◽  
Combinatorial Optimization Problems ◽  
Feasible Solutions

In this paper, we develop TCGD, a problem-independent, time-constrained, approximate guided depth-first search (GDFS) algorithm. The algorithm is designed to achieve the best ascertained approximation degree under a fixed time constraint. We consider only searches with finite search space and admissible heuristic functions. We study NP-hard combinatorial optimization problems with polynomial-time computable feasible solutions. For the problems studied, we observe that the execution time increases exponentially as approximation degree decreases, although anomalies may happen. The algorithms we study are evaluated by simulations using the symmetric traveling-salesperson problem.

SOLVING A SINGLE MACHINE SCHEDULING PROBLEM BY A DISCRETE VERSION OF ELECTROMAGNETISM-LIKE METHOD

2009 ◽  
Vol 18 (08) ◽  
pp. 1597-1608 ◽  
Author(s):  
NIKBAKHSH JAVADIAN ◽  
MOHSEN GOLALIKHANI ◽  
REZA TAVAKKOLI-MOGHADDAM
Keyword(s):  
Combinatorial Optimization ◽  
Single Machine ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Machine Scheduling ◽  
Population Based ◽  
Single Machine Scheduling ◽  
Discrete Version ◽  
Scheduling Problem ◽  
Combinatorial Optimization Problems

The electromagnetism-like method (EM) is a population based meta-heuristic algorithm utilizing an attraction-repulsion mechanism to move sample points (i.e., our solutions) towards the optimality. In general, the EM has been initially used for solving continuous optimization problems and could not be applied on combinatorial optimization ones. This paper proposes a discrete binary version of the EM for solving combinatorial optimization problems. To show the efficiency of our proposed EM, we solve a single machine scheduling problem and compare our computational results with the solutions reported in the literature. Finally, we conclude that our proposed method is capable of solving such well-known problems more efficiently than the previous studies.

Implementacija algoritma kolonije mrava za optimalnu rekonfiguraciju distributivne mreže

2020 ◽  
Vol 22 (1-2) ◽  
pp. 50-57
Author(s):  
◽  
Nikola Rajaković
Keyword(s):  
Combinatorial Optimization ◽  
Distribution Systems ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Population Based ◽  
Active Power ◽  
Search Technique ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
Heuristic Technique

The paper analyzes the possibility of application of Ant Colony Optimization (ACO) algorithm for reconfiguration of distribution network with the aim of active power minimiza- tion. ACO is a population-based meta-heuristic technique used to solve different combinatorial optimization problems. The search technique is inspired by the behaviour of ant colonies in nature. The efficiency of the proposed algorithm is demonstrated on IEEE 33-bus and IEEE 69-bus test distribution systems. Also, the results obtained by using ACO algorithm are compared to the results achievable by other heuristic and meta-heuristic algorithms.

Search-space smoothing for combinatorial optimization problems

1997 ◽  
Vol 243 (1-2) ◽  
pp. 77-112 ◽  
Author(s):  
Johannes Schneider ◽  
Markus Dankesreiter ◽  
Werner Fettes ◽  
Ingo Morgenstern ◽  
Martin Schmid ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Search Space ◽  
Combinatorial Optimization Problems

A New Differential Evolution Based Metaheuristic for Discrete Optimization

2010 ◽  
Vol 1 (2) ◽  
pp. 15-32 ◽  
Author(s):  
Ricardo Sérgio Prado ◽  
Rodrigo César Pedrosa Silva ◽  
Frederico Gadelha Guimarães ◽  
Oriane Magela Neto
Keyword(s):  
Combinatorial Optimization ◽  
Differential Evolution ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Search Space ◽  
Combinatorial Problems ◽  
Combinatorial Optimization Problems ◽  
De Algorithm ◽  
The Difference

The Differential Evolution (DE) algorithm is an important and powerful evolutionary optimizer in the context of continuous numerical optimization. Recently, some authors have proposed adaptations of its differential mutation mechanism to deal with combinatorial optimization, in particular permutation-based integer combinatorial problems. In this paper, the authors propose a novel and general DE-based metaheuristic that preserves its interesting search mechanism for discrete domains by defining the difference between two candidate solutions as a list of movements in the search space. In this way, the authors produce a more meaningful and general differential mutation for the context of combinatorial optimization problems. The movements in the list can then be applied to other candidate solutions in the population as required by the differential mutation operator. This paper presents results on instances of the Travelling Salesman Problem (TSP) and the N-Queen Problem (NQP) that suggest the adequacy of the proposed approach for adapting the differential mutation to discrete optimization.

Sign in / Sign up

Export Citation Format

Share Document