Alfonsas Misevičius

An Improved Hybrid Genetic Algorithm: New Results for the Quadratic Assignment Problem

Genetic algorithms (GAs) have been proven to be among the most powerful intelligent techniques in various areas of the computer science, including difficult optimization problems. In this paper, we propose an improved hybrid genetic algorithm (IHGA). It uses a robust local improvement procedure (a limited iterated tabu search (LITS)) as well as an effective restart (diversification) mechanism that is based on so-called “shift mutations”. IHGA has been applied to the well-known combinatorial optimization problem, the quadratic assignment problem (QAP). The results obtained from the numerous experiments on different QAP instances from the instances library QAPLIB show that the proposed algorithm appears to be superior to other modem heuristic approaches that are among the best algorithms for the QAP. The high efficiency of our algorithm is also corroborated by the fact that the new, recordbreaking solutions were obtained for a number of large real-life instances.

A tabu search algorithm for the quadratic assignment problem

Tabu search approach based algorithms are among the widest applied to various combinatorial optimization problems. In this paper, we propose a new version of the tabu search algorithm for the well-known problem, the quadratic assignment problem (QAP). One of the most important features of our tabu search implementation is an efficient use of mutations applied to the best solutions found so far. We tested this approach on a number of instances from the library of the QAP instances—QAPLIB. The results obtained from the experiments show that the proposed algorithm belongs to the most efficient heuristics for the QAP. The high efficiency of this algorithm is also demonstrated by the fact that the new best known solutions were found for several QAP instances.

Genetic algorithm hybridized with ruin and recreate procedure: application to the quadratic assignment problem

Genetic algorithms (GAs) are among the widely used in various areas of computer science, including optimization problems. In this paper, we propose a GA hybridized with so-called ruin and recreate (R and R) procedure. We have applied this new hybrid strategy to the well-known combinatorial optimization problem, the quadratic assignment problem (QAP). The results obtained from the experiments on different QAP instances show that the proposed algorithm belongs to the best heuristics for the QAP. The power of this algorithm is also demonstrated by the fact that the new best known solutions were found for several QAP instances.

An implementation of the iterated tabu search algorithm for the quadratic assignment problem

In this paper, we describe an implementation of the iterated tabu search (ITS) algorithm for the quadratic assignment problem (QAP), which is one of the well-known problems in combinatorial optimization. The medium- and large-scale QAPs are not, to this date, practically solvable to optimality, therefore heuristic algorithms are widely used. In the proposed ITS approach, intensification and diversification mechanisms are combined in a proper way. The goal of intensification is to search for good solutions in the neighbourhood of a given solution, while diversification is responsible for escaping from local optima and moving towards new regions of the search space. In particular, the following enhancements were implemented: new formula for fast evaluation of the objective function and efficient data structure; extended intensification mechanisms (including randomized tabu criterion, combination of tabu search and local search, dynamic tabu list maintaining); enhanced diversification strategy using periodic tabu tenure and special mutation procedure. The ITS algorithm is tested on the different instances taken from the QAP library QAPLIB. The results from the experiments demonstrate promising efficiency of the proposed algorithm, especially for the random QAP instances.

Enhancing the performance of hybrid genetic algorithms by differential improvement

A differential improvement modification to Hybrid Genetic Algorithms is proposed. The general idea is to perform more extensive improvement algorithms on higher quality solutions. Our proposed Differential Improvement (DI) approach is of rather general character. It can be implemented in many different ways. The paradigm remains invariant and can be easily applied to a wider class of optimization problems. Moreover, the DI framework can also be used within other Hybrid metaheuristics like Hybrid Scatter Search algorithms, Particle Swarm Optimization, or Bee Colony Optimization techniques. Extensive experiments show that the new approach enables to improve significantly the performance of Hybrid Genetic Algorithms without adding extra computer time. Additional experiments investigated the trade-off between the number of generations and the number of iterations of the improvement algorithm. These experiments yielded six new best known solutions to benchmark quadratic assignment problems. Many other variants of the proposed algorithm are suggested for future research.

An improved hybrid optimization algorithm for the quadratic assignment problem

Abstract In this paper, we present an improved hybrid optimization algorithm, which was applied to the hard combinatorial optimization problem, the quadratic assignment problem (QAP). This is an extended version of the earlier hybrid heuristic approach proposed by the author. The new algorithm is distinguished for the further exploitation of the idea of hybridization of the well‐known efficient heuristic algorithms, namely, simulated annealing (SA) and tabu search (TS). The important feature of our algorithm is the so‐called “cold restart mechanism”, which is used in order to avoid a possible “stagnation” of the search. This strategy resulted in very good solutions obtained during simulations with a number of the QAP instances (test data). These solutions show that the proposed algorithm outperforms both the “pure” SA/TS algorithms and the earlier authors combined SA and TS algorithm. Key words: hybrid optimization, simulated annealing, tabu search, quadratic assignment problem, simulation

GENERATION OF GREY PATTERNS USING AN IMPROVED GENETICEVOLUTIONARY ALGORITHM: SOME NEW RESULTS

Genetic and evolutionary algorithms have achieved impressive success in solving various optimization problems. In this work, an improved genetic-evolutionary algorithm (IGEA) for the grey pattern problem (GPP) isdiscussed. The main improvements are due to the specific recombination operator and the modified tabu search (intraevolutionary) procedure as a post-recombination algorithm, which is based on the intensification and diversification methodology. The effectiveness of IGEA is corroborated by the fact that all the GPP instances tested are solved topseudo-optimality at very small computational effort. The graphical illustrations of the grey patterns are presented. http://dx.doi.org/10.5755/j01.itc.40.4.983

An Intensive Search Algorithm for the Quadratic Assignment Problem

Many heuristics, such as simulated annealing, genetic algorithms, greedy randomized adaptive search procedures are stochastic. In this paper, we propose a deterministic heuristic algorithm, which is applied to the quadratic assignment problem. We refer this algorithm to as intensive search algorithm (or briefly intensive search). We tested our algorithm on the various instances from the library of the QAP instances – QAPLIB. The results obtained from the experiments show that the proposed algorithm appears superior, in many cases, to the well-known algorithm – simulated annealing.

Expansion of the minimum-inbreeding seed orchard design to operational scale

The minimum-inbreeding (MI) seed orchard layout, formulated originally as a global quadratic assignment problem, was expanded into realistic problem sizes that are often encountered in operational forestry, where two modifications were tested: (1) the merging algorithm of independent MI’s solutions (i.e., smaller blocks) and (2) the extended global (genetic-tabu) algorithm. Extending the global heuristic algorithm of the quadratic assignment problem seems to be the most efficient strategy. The reported minimum-inbreeding distance of the extended MI scheme was the lowest in comparison to the completely randomized and the randomized, replicated, staggered clonal-row (R2SCR) seed orchard design schemes. These conclusions also hold for more complex scenarios when added relatedness among orchard’s parents or unequal deployment was considered. This improved MI scheme is suitable to large and complex advanced-generation seed orchards, where many practical constraints have to be jointly considered.

Computational Study of Four Genetic Algorithm Variants for Solving the Quadratic Assignment Problem

Genetic algorithms (GAs) are a modern class of the metaheuristic methods that have been applied for the solution of different combinatorial optimization problems, among them, the quadratic assignment problem (QAP). Various modifications and alternatives of the genetic algorithms have been proposed in the artificial intelligence literature, and in many cases they performed quite successfully and efficiently. In this paper, we describe four variants of the genetic algorithm and some more or less slight variations of these variants. We also present the results of the computational experiments with both the real life like and randomly generated instances from the library of the benchmark QAP instances – QAPLIB.