A hybrid heuristic solving the traveling salesman problem

This paper presents a new hybrid heuristic for solving the Traveling Salesman Problem, The algorithm is designed on the frame of a general optimization procedure which acts upon two steps, iteratively. In first step of the global search, a feasible tour is constructed based on insertion approach. In the second step the feasible tour found at the first step, is improved by a local search optimization procedure. The second part of the paper presents the performances of the proposed heuristic algorithm, on several test instances. The statistical analysis shows the effectiveness of the local search optimization procedure, in the graphical representation.peer-reviewe

A hybrid shifting bottleneck-tabu search heuristic for the job shop total weighted tardiness problem

In this paper, we study the job shop scheduling problem with the objective of minimizing the total weighted tardiness. We propose a hybrid shifting bottleneck - tabu search (SB-TS) algorithm by replacing the reoptimization step in the shifting bottleneck (SB) algorithm by a tabu search (TS). In terms of the shifting bottleneck heuristic, the proposed tabu search optimizes the total weighted tardiness for partial schedules in which some machines are currently assumed to have infinite capacity. In the context of tabu search, the shifting bottleneck heuristic features a long-term memory which helps to diversify the local search. We exploit this synergy to develop a state-of-the-art algorithm for the job shop total weighted tardiness problem (JS-TWT). The computational effectiveness of the algorithm is demonstrated on standard benchmark instances from the literature

A hybrid algorithm for k-medoid clustering of large data sets

In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach

A Hybrid Framework for Heuristic Research: The Travelling Salesperson Problem

The research community is confusing research and development: with competitive experiment suited only for the latter. This realisation led to the call for the revision of the current TSP heuristic investigation framework which researchers believe is biased towards development frameworks. However, despite the wide spread debate on the subject, minimum attempts to correct the situation have been done may be due to lack of necessary information required to implement heuristic research frameworks. This thesis, therefore, develops and implements a hybrid TSP heuristic research framework which amalgamates the two frameworks. The implementation process involves conducting heuristic experiments and classification, developing a novel data analysis tool and hybrid metaheuristic and statistically comparing heuristic performances to determine the best heuristic and its features. Surveys on the TSP implemented heuristics and variants and investigation frameworks applied are conducted. The heuristic classification develops a standard scheme and its classifying templates. A thorough statistical comparison of heuristic performances produces results that prompt debatable remarks. One of them is that heuristics tend to reach an absorption stage during the search for the global optimum solution and thus require a mechanism to drag them out of the trapping search space. The other remark is that the ANOVA assumptions are irrelevant. The reliability analysis reveals that heuristic performances are unpredictable. The Simulated Annealing is the best heuristic. However, other metaheuristics can not be dismissed because they performed statistically the same in many cases. The work designs a Hybrid Erosion And Deposition (HEAD) metaheuristic. The new discovery employs the Tabu Search, Simulated Annealing, Ant Colony, constructive heuristic, central management and erosion and deposition dynamics. These features are amalgamated into a three phased loop (Evaluation, Development and Improvement) which improves the initial solution developed by the constructive heuristic. This thesis develops a hybrid heuristic research framework. It also contributes towards clarification of the misconception between research and development frameworks, thus, making available the vital information hindering the implementation of research frameworks. This study suggests that more scientific researches should be conducted in statistical data analysis, violation of ANOVA assumptions and application of matrix instances

A hybrid scatter search. Electromagnetism meta-heuristic for project scheduling.

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-optimal heuristic solutions. The procedure combines elements from scatter search, a generic population-based evolutionary search method, and a recently introduced heuristic method for the optimisation of unconstrained continuous functions based on an analogy with electromagnetism theory, hereafter referred to as the electromagnetism meta-heuristic. We present computational experiments on standard benchmark datasets, compare the results with current state-ofthe-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also demonstrate that the algorithm outperforms state-of-the-art existing heuristics.Algorithms; Effectiveness; Electromagnetism; Functions; Heuristic; Project scheduling; Scatter; Scatter search; Scheduling; Theory;