Swapping algorithm and meta-heuristic solutions for combinatorial optimization n-queens problem

This research proposes the swapping algorithm a new algorithm for solving the n-queens problem, and provides data from experimental performance results of this new algorithm. A summary is also provided of various meta-heuristic approaches which have been used to solve the n-queens problem including neural networks, evolutionary algorithms, genetic programming, and recently Imperialist Competitive Algorithm (ICA). Currently the Cooperative PSO algorithm is the best algorithm in the literature for finding the first valid solution. Also the research looks into the effect of the number of hidden nodes and layers within neural networks and the effect on the time taken to find a solution. This paper proposes a new swapping algorithm which swaps the position of queens

Hybridization of Bat and Genetic Algorithm to Solve N-Queens Problem

In this paper, a hybrid of Bat-Inspired Algorithm (BA) and Genetic Algorithm (GA) is proposed to solve N-queens problem. The proposed algorithm executes the behavior of microbats with changing pulse rates of emissions and loudness to final all the possible solutions in the initialization and moving phases. This dataset applied two metaheuristic algorithms (BA and GA) and the hybrid to solve N-queens problem by finding all the possible solutions in the instance with the input sizes of area 8*8, 20*20, 50*50, 100*100 and 500*500 on a chessboard. To find the optimal solution, consistently, ten run have been set with 100 iterations for all the input sizes. The hybrid algorithm obtained substantially better results than BA and GA because both algorithms were inferior in discovering the optimal solutions than the proposed randomization method. It also has been discovered that BA outperformed GA because it requires a reduced amount of steps in determining the solutions

Ordered greed II: graph coloring

A popular application of genetic algorithms (GAs) is to attempt to generate good, rapid, approximate solutions to NP-complete or NP-hard problems. Previously, in [1], and [4], we introduced a hybrid algorithm combining a GA with simple greedy algorithms applied to the N-Queens problem and to sports tournament scheduling. The greedy algorithm makes locally optimal assignments (to Queens or matches) in some order. We treat that ordering as the sought after goal, and thus work with a population whose individuals are permutations. The subject of the present paper is the problem of graph coloring. We focus the present paper on the single benchmark problem of coloring a three-colorable graph that was constructed as a subgraph of the complete 3-partite graph Kp,q,r in which each edge exists with probability 0.1. (We have applied our method successfully to several other categories of graphs, but present space limitations dictate presenting the results for this special case.) (Refer to PDF file for exact formulas

A Solution to the N-Queens Problem Using Biogeography-Based Optimization

Biogeography-based Optimization (BBO) is a global optimization algorithm based on population, governed by mathematics of biogeography, and dealing with geographical distribution of biological organisms. The BBO algorithm was used in the present study to provide a solution for the N-queens problem. The performance of the proposed algorithm has been evaluated in terms of the quality of the obtained results, cost function, and execution time. Furthermore, the results of this algorithm were compared against those of genetic and particle swarm algorithms

Generic chromosome representation and evaluation for genetic algorithms

The past thirty years have seen a rapid growth in the popularity and use of Genetic Algorithms for searching for optimal or near-optimal solutions to optimisation problems. One of the reasons for their immense success is the fact that the principles governing the algorithm are simple enough to be appreciated and understood. The major differences between one Genetic Algorithm and another lie within the schemes used to represent chromosomes, the semantics of the genetic operators, and the measures used to evaluate their fitness. Yet, these very differences make Genetic Algorithms so complex to design and implement when opposed with most real-world optimisation problems. The truth is that the people faced with these types of optimisation problems are not necessarily computer sci- entists or machine learning experts. Indeed, these types of problems constantly appear in various non-computing disciplines ranging from biology to manufacturing and economics. In this report, we present a simple, yet powerful, high-level technique that can be used to describe the structure of chromosomes and how their fitness can be evaluated. The method is abstract enough to insulate the practitioner from all the implementation, design, and coding details usually associated with a Genetic Algorithm. Nonetheless, a wide array of optimisation problems ranging from the classical travelling salesman problem and the n-Queens problem to time-table scheduling and dynamic programs can be described.peer-reviewe

Genetic Algorithms - Multi-core CPU Implementation

Cílem diplomové práce je vytvořit co možná nejuniverzálnější knihovnu pro genetické algoritmy v jazyce C++, s určitým počtem implementovaných univerzálních operátorů a následně vytvořenou knihovnu otestovat na příkladech. Musí být implementována podpora více-jádrových procesorů pomocí OpenMP. Knihovna bude testována celkově na třech příkladech. První dva příklady jsou matematické funkce, které se používají právě k testování genetických algoritmů. Dalším testovacím příkladem je problém rozložení n-dam na šachovnici, aby se vzájemně neohrožovali. Nakonec se pokusíme pomocí navrhnutých algoritmů zjistit řešení puzzle s názvem Eternity II, za jehož vyřešení je vypsána odměna 2 milióny dolarů.his diploma thesis deals with creating the most universal library of genetic algorithms in C++, as much as possible, implemented with the certain number of universal operators, and then with testing created library on some examples. Library must support multi-core processors, implementation will be done over OpenMP. The library will be tested on three examples in all. The first two examples are mathematical functions, that are used just for genetic algorithms testing. Last problem for test is N-Queens problem. Finally we will use genetic algorithms to try find solution for Eternity II puzzle, there is declared a 2 million bounty for full solution.

Ordered greed

Scheduling problems are among the most challenging and realistic problems application of problem solving heuristics, such as genetic algorithms (GAs). The naive greedy algorithm for scheduling simply assigns, in turn, each item to be scheduled the best yet untaken position for that item. We investigate using a genetic algorithm to search the space of orderings for this greedy algorithm. That is, the GA individuals are permuations that determine the permutations that are the schedules, rather than the GA individuals directly being the schedules. We have experimented with the classical N Queens problem anda realistic soccer tournament scheduling problem, comparing the GA individual as the assignment with our greedy hybrid algorithm ( ordered greed ). Warnsdorff\u27s heuristic is introduced to modify blind greed with excellent results. We also introduce the use of signatures in our GAs to represent permutations. Signatures are easy to create and manipulate in crossover and mutation operations

A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

Real world combinatorial optimization problems such as scheduling are typically too complex to solve with exact methods. Additionally, the problems often have to observe vaguely specified constraints of different importance, the available data may be uncertain, and compromises between antagonistic criteria may be necessary. We present a combination of approximate reasoning based constraints and iterative optimization based heuristics that help to model and solve such problems in a framework of C++ software libraries called StarFLIP++. While initially developed to schedule continuous caster units in steel plants, we present in this paper results from reusing the library components in a shift scheduling system for the workforce of an industrial production plant.Comment: 33 pages, 9 figures; for a project overview see http://www.dbai.tuwien.ac.at/proj/StarFLIP