Comparison Of Metaheuristics Research Articles

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.

Flight scheduling incorporating bad weather conditions through big data analytics: A comparison of metaheuristics

AbstractThe most important mode of long‐distance transportation for people to use time efficiently is air transportation. One of the most significant factors determining air traffic performance is the scheduling created by companies. When creating schedules, various parameters are used, mainly the demand for the determined route, and the availability of airplanes and flight crews. Known climatic conditions on the route are generally neglected. Within the scope of this study, the creation of flight schedules, which are normally the preparation of flight schedules by considering the monthly, daily, and hourly constraints that could cause flight delays, were determined by also considering weather conditions. In this determination process, 32‐year flight data between 1987 and 2018 were used as a basis. Apache Spark, one of the big data technologies, was used to determine these constraints. The scheduling was optimized by using the constraints used to prevent flight delays. It was observed in the results obtained that simulated annealing (SA) achieved the most optimal results compared to the genetic algorithm (GA) and the artificial bee colony (ABC) algorithm. As the dimensions of the searched space increased, solving the problem with metaheuristic approaches was more advantageous in terms of time compared to the classical method.

A multi-objective pharmaceutical supply chain network based on a robust fuzzy model: A comparison of meta-heuristics

The pharmaceutical supply chain has features that distinguish it from other supply chains. Medicine is considered a strategic commodity, and the smallest disruption in its supply chain may cause severe crises. This is why the distribution of pharmaceutical products needs to combine the minimization of costs with strong compliance with service standards while taking into account risks due to uncertainty. In this study, we present a new multi-objective multi-echelon multi-product multi-period pharmaceutical supply chain network (PSCN) along with the production–distribution–purchasing–ordering–inventory holding-allocation-routing problem under uncertainty. We formulate the problem as a Mixed-Integer Non-Linear Programming model and develop a novel robust fuzzy programming method to cope with uncertainty parameters. To find optimal solutions, several multi-objective metaheuristic algorithms, namely, MOSEO, MOSAM MOKA, and MOFFA considering different criteria and multi-objective assessment metrics are suggested. Since there are no benchmarks existing in the literature, 10 numerical instances in large and small sizes are generated and also the trapezoidal fuzzy numbers of the uncertain parameters were randomly generated based on a uniform distribution. The required parameters were set and also the simulated data were examined in an exact method and by metaheuristic algorithms. The results confirm the efficiency of the MOFFA algorithm to detect a near-optimal solution within a logical CPU time. The solution methods are complemented with several sensitivity analyses on the input parameters of the proposed model.

용량제한을 갖는 차량경로 문제에 대한 메타 휴리스틱의 비교 분석

용량제한을 갖는 차량경로 문제에 대한 메타 휴리스틱의 비교 분석

Comparison of metaheuristics and dynamic programming for district energy optimization

Metaheuristic optimization methods, as model-free methods, are expected to be applicable to practical issues (e.g., engineering problems). Although optimization methods have been proposed or improved through many theoretical studies, they should be tested using not only some benchmark functions, but also other models representing practical situations, such as those involving discrete control variables and equality or inequality constraints. Hence, differential evolution (DE)-based constrained optimization methods were applied to district energy optimization in this study. Several different types of DE-based methods and dynamic programming which was utilized to obtain theoretical results, were compared. The proposed DE-based method, ε-constrained DE with random jumping II (εDE-RJ-II), proved capable of producing results differing by only 2.1% from the theoretical results in a computation time 1/457 of that required by dynamic programming. Therefore, εDE-RJ-II has high potential to provide comprehensive district energy optimization within a realistic computation time.

Comparison of metaheuristics to measure gene effects on phylogenetic supports and topologies

BackgroundA huge and continuous increase in the number of completely sequenced chloroplast genomes, available for evolutionary and functional studies in plants, has been observed during the past years. Consequently, it appears possible to build large-scale phylogenetic trees of plant species. However, building such a tree that is well-supported can be a difficult task, even when a subset of close plant species is considered. Usually, the difficulty raises from a few core genes disturbing the phylogenetic information, due for example from problems of homoplasy. Fortunately, a reliable phylogenetic tree can be obtained once these problematic genes are identified and removed from the analysis.Therefore, in this paper we address the problem of finding the largest subset of core genomes which allows to build the best supported tree.ResultsAs an exhaustive study of all core genes combination is untractable in practice, since the combinatorics of the situation made it computationally infeasible, we investigate three well-known metaheuristics to solve this optimization problem. More precisely, we design and compare distributed approaches using genetic algorithm, particle swarm optimization, and simulated annealing. The latter approach is a new contribution and therefore is described in details, whereas the two former ones have been already studied in previous works. They have been designed de novo in a new platform, and new experiments have been achieved on a larger set of chloroplasts, to compare together these three metaheuristics.ConclusionsThe ways genes affect both tree topology and supports are assessed using statistical tools like Lasso or dummy logistic regression, in an hybrid approach of the genetic algorithm. By doing so, we are able to provide the most supported trees based on the largest subsets of core genes.

Comparison of metaheuristics on multi objective (Cost-C02) optimization of RC cantilever retaining walls

Comparison of metaheuristics on multi objective (Cost-C02) optimization of RC cantilever retaining walls

A Comparative Study of Simulated Annealing and Genetic Algorithm for Solving the Travelling Salesman Problem

Metaheuristic algorithms have proved to be good solvers for the traveling salesman problem (TSP). All metaheuristics usually encounter problems on which they perform poorly so the programmer must gain experience on which optimizers work well in different classes of problems. However due to the unique functionality of each type of meta-heuristic, comparison of metaheuristics is in many ways more difficult than other algorithmic comparisons. In this paper, solution to the traveling salesman problem was implemented using genetic algorithm and simulated annealing. These algorithms were compared based on performance and results using several benchmarks. It was observed that Simulated Annealing runs faster than Genetic Algorithm and runtime of Genetic Algorithm increases exponentially with number of cities. However, in terms of solution quality Genetic Algorithm is better than Simulated Annealing.

Comparison of meta-heuristics for container ship routing problem

Container ship routing problem is a special variant of vehicle routing problem (VRP) which needs a lot of attention which costs enormous amount of money per day. In this paper, two popular meta-heuristics namely genetic algorithm (GA) and simulated annealing (SA) for solving the ship routing problem have been compared. A series of experiments were conducted varying the number of ports involved and the route taken to visit these ports. These numerical experiments revealed that SA yielded more profits compared to GA as number of ports increased.

A general-purpose tunable landscape generator

The research literature on metaheuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metaheuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator

A comprehensive review and evaluation of permutation flowshop heuristics

In this work we present a review and comparative evaluation of heuristics and metaheuristics for the well-known permutation flowshop problem with the makespan criterion. A number of reviews and evaluations have already been proposed. However, the evaluations do not include the latest heuristics available and there is still no comparison of metaheuristics. Furthermore, since no common benchmarks and computing platforms are used, the results cannot be generalised. We propose a comparison of 25 methods, ranging from the classical Johnson's algorithm or dispatching rules to the most recent metaheuristics, including tabu search, simulated annealing, genetic algorithms, iterated local search and hybrid techniques. For the evaluation we use the standard test of Taillard [Eur. J. Operation. Res. 64 (1993) 278] composed of 120 instances of different sizes. In the evaluations we use the experimental design approach to obtain valid conclusions on the effectiveness and efficiency of the different methods tested.

A comprehensive review and evaluation of permutation flowshop heuristics

In this work we present a review and comparative evaluation of heuristics and metaheuristics for the well-known permutation flowshop problem with the makespan criterion. A number of reviews and evaluations have already been proposed. However, the evaluations do not include the latest heuristics available and there is still no comparison of metaheuristics. Furthermore, since no common benchmarks and computing platforms are used, the results cannot be generalised. We propose a comparison of 25 methods, ranging from the classical Johnson's algorithm or dispatching rules to the most recent metaheuristics, including tabu search, simulated annealing, genetic algorithms, iterated local search and hybrid techniques. For the evaluation we use the standard test of Taillard [Eur. J. Operation. Res. 64 (1993) 278] composed of 120 instances of different sizes. In the evaluations we use the experimental design approach to obtain valid conclusions on the effectiveness and efficiency of the different methods tested.

Optimal Design of Reliable Computer Networks: A Comparison of Metaheuristics

In many computer communications network design problems, such as those faced by hospitals, universities, research centers, and water distribution systems, the topology is fixed because of geographical and physical constraints or the existence of an existing system. When the topology is known, a reasonable approach to design is to select components among discrete alternatives for links and nodes to maximize reliability subject to cost. This problem is NP-hard with the added complication of a very computationally intensive objective function. This paper compares the performance of three classic metaheuristic procedures for solving large and realistic versions of the problem: hillclimbing, simulated annealing and genetic algorithms. Three alterations that use local search to seed the search or improve solutions during each iteration are also compared. It is shown that employing local search during evolution of the genetic algorithm, a memetic algorithm, yields the best network designs and does so at a reasonable computational cost. Hillclimbing performs well as a quick search for good designs, but cannot identify the most superior designs even when computational effort is equal to the metaheuristics.