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International Journal of Metaheuristics (5 papers in press)
Hybrid metaheuristics for the periodic open arc routing problem by Ali Kansou, Bilal Kanso Abstract: This work considers the periodic open arc routing problem (POCARP) that models the meter reader application. This application is very interesting when the routes are planned on horizon of several periods. We develop two approaches to solve the problem under study: the first one is based on hybrid genetic algorithm with a specific crossover and the second one on hybrid ant colony method combined with an insertion heuristic. The two proposed algorithms are hybridised with a local search procedure that exploits several moves (relocate, swap, 2-opt and change combination). The objective of the problem is to find a combination of service periods for each task as well as the feasible routes of each period by using a predefined number of available vehicles that minimise the total travelling distance over the multi period horizon. We extended the optimal splitting procedure to generate and evaluate solutions. We compared our approaches with one of the most important insertion heuristics adapted to this problem. Computational experiments are conducted on a set of generated benchmark instances and indicate that the proposed metaheuristics dominate the good insertion heuristic. Keywords: metaheuristics; open arc routing problem; multi period; genetic algorithm; ant colony algorithm. DOI: 10.1504/IJMHEUR.2020.10034062
Solving the Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints using a Parallel VNS approach by Ines Sbai, Saoussen Krichen, Olfa Limam Abstract: A combination of the two most important problems in distribution logistics called the Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraint is studied in this article. The objective consists in designing the most appropriate path ways, starting and terminating at a central depot, minimizing the total transportation cost with a homogenous fleet of vehicles based on a depot node. Items loaded in each vehicles trip must satisfy the twodimensional of orthogonal packing constraint. Since, the problem is one of the classical NP-Hard optimization problems, a large number of approaches have been proposed. Whereas, finding exact solution via optimisation is still a challenging problem due to their complexity. So, in order to increase the exploration in the solution space, we propose a variant of a variable neighborhood search called the Parallel Variable Neighborhood Search. Our algorithm is tested with 180 benchmark instances and compared with state-of-the-art approaches. Results shown that our proposed approach is competitive in terms of the quality of the solutions found. Keywords: Routing; Packing; Variable neighborhood search; 2L-CVRP; VehiclernRouting Problem.
Variable neighborhood search for binary integer programming problems by Håkon Bentsen, Lars Magnus Hvattum Abstract: General solvers exist for several types of optimization problems, with the commercially available solvers for mixed integer programming (MIP) being a prime example. Although binary integer programming (BIP) can be used to model a wide variety of important combinatorial optimization problems, relatively few contributions have been made to develop heuristic algorithms for BIP. This paper examines whether variable neighborhood search can be successfully used to tackle BIP instances, when avoiding very large neighborhoods explored by the means of external MIP solvers. The results indicate that methods based on variable neighborhood search are more successful than exact and heuristic commercial solvers on certain types of instances, while the opposite holds true on others. A general variable neighborhood search proves very effective on instances with up to 200 variables, in particular some instances that are tightly constrained. Keywords: black-box solver; 0-1 integer programming; variable neighborhood descent; mathematical programming.
A Multi-stage Genetic Algorithm for Instance selection dedicated to K Nearest Neighbors Classification: Application to Robot Wall Following Problem by Sarah Madi, Ahmed Riadh Baba-Ali Abstract: K nearest neighbours algorithm is a classic, well studied yet a promising classification technique with high accuracy and best learning time compared to other classification algorithms. The goal is to overcome its slow classification time using instance selection by eliminating redundant and erroneous data. The instance selection problem has been classified as a non-deterministic polynomial time hard problem. In this article, the aim is to keep up with real time applications such as robotics with limited memory, while maintaining the fast learning speed and high classification accuracy. We introduce a new multistage genetic algorithm for instance selection consisting of successive genetic instance selection stages with iterative search space reduction. The results witnessed an extreme reduction in classification time, reaching 99% without any significant penalty in the accuracy. It has been tested successfully using real traces of robot wall following navigation datasets and favourably compared to other approaches using various datasets. Keywords: machine learning; instance selection; K nearest neighbours; KNN classification; genetic algorithm; wall following.
Special Issue on: Analysis and Implementation of Nature-Inspired Algorithms
Twitter Sentiment analysis using hybrid Grey Wolf Optimizer method by Rekha Kushwaha Abstract: In recent years, the use of social media has increased excessively. A large amount of data expressing the feelings of millions of people is available on social media. Sentiment Analysis (SA) is one of the mechanisms to analyze these feelings. It is an outstanding field of data mining which concerns the recognition and interpretation of sentiments available on social media. This paper is concerned about the extraction of sentiments from the well known social media website, Twitter. On Twitter, people can express their opinions in the term of tweets. Tweets can be either a review or a comment possibly, which can be +ve, -ve or neutral. Because of the subjective behaviour of tweets, sentiments analysis is considered as a complex problem that is very difficult to deal with the available conventional strategies. In this paper, a hybrid mechanism is introduced, namely Hybrid Grey-Wolf-Optimizer with K-means clustering (GWOK) to find the optimal heads of the clusters of the available dataset. The accuracy and efficiency of the proposed mechanism are analyzed on two datasets: sander2 and twitter dataset. The obtained outcomes of the proposed mechanism are compared with some state-of-art approaches of Nature-Inspired algorithms such as Particle-Swarm algorithm, Genetic-Algorithm, Cuckoo-Search, and Differential Evolution. These outcomes prove the efficiency and accuracy of the proposed mechanism to solve the Twitter sentiment analysis problem. Keywords: Sentiment-Analysis (SA); Twitter; Nature-Inspired-Algorithm; Machine learning techniques; Optimization; K-means clustering; Grey-Wolf-Optimizer;.