Doctoral dissertation. Fenghe, J. Ghiani, G. Introduction to Logistics Systems Planning and Control. Chichester, England: J. Ghoseiri, K. Golberg, D. Massachusetts: Addion Wesley Publ. Golden, B.
Handbook of Heuristics
Journal Operation Research, Vol. Halse, K. PhD Thesis. Han, S. Asia Pacific Management Review, Vol. Holland, J. MIT Press. Jianjun, L. Joshi, S. Kallehauge, B.
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Man, K. B R Rajakumar. D Binu. Cite Citation. Permissions Icon Permissions. Abstract This paper proposes a novel meta-heuristic algorithm, named DHOA, which is inspired by the hunting behavior of humans toward deer. Issue Section:. Handling Editor: Domenico Rosaci. You do not currently have access to this article.
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Download all figures. Sign in. You could not be signed in. Sign In Forgot password? Don't have an account? Sign in via your Institution Sign in. Purchase Subscription prices and ordering Short-term Access To purchase short term access, please sign in to your Oxford Academic account above. This chapter presents a literature review of the main advances in the field of hyper-heuristics, since the publication of a survey paper in The chapter demonstrates the most recent advances in hyper-heuristic foundations, methodologies, theory, and application areas.
In addition, a simple illustrative selection hyper-heuristic framework is developed as a case study.
This is based on the well-known Iterated Local Search algorithm and is presented to provide a tutorial style introduction to some of the key basic issues. A brief discussion about the implementation process in addition to the decisions that had to be made during the implementation is presented. The framework implements an action selection model that operates on the perturbation stage of the Iterated Local Search algorithm to adaptively select among various low-level perturbation heuristics.
The performance and efficiency of the developed framework is evaluated across six well-known real-world problem domains. Iterated greedy is a search method that iterates through applications of construction heuristics using the repeated execution of two main phases, the partial destruction of a complete candidate solution and a subsequent reconstruction of a complete candidate solution. Iterated greedy is based on a simple principle, and methods based on this principle have been proposed and published several times in the literature under different names such as simulated annealing, iterative flattening, ruin-and-recreate, large neighborhood search, and others.
Despite its simplicity, iterated greedy has led to rather high-performing algorithms.
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In combination with other heuristic optimization techniques such as a local search, it has given place to state-of-the-art algorithms for various problems. This paper reviews the main principles of iterated greedy algorithms, relates the basic technique to the various proposals based on this principle, discusses its relationship with other optimization techniques, and gives an overview of problems to which iterated greedy has been successfully applied.
Iterated local search is a metaheuristic that embeds an improvement heuristic within an iterative process generating a chain of solutions. Often, the improvement method is some kind of local search algorithm and, hence, the name of the metaheuristic. The iterative process in iterated local search consists in a perturbation of the current solution, leading to some intermediate solution that is used as a new starting solution for the improvement method.
An additional acceptance criterion decides which of the solutions to keep for continuing this process. This simple idea has led to some very powerful algorithms that have been successfully used to tackle hard combinatorial optimization problems. In this chapter, we review the main ideas of iterated local search, exemplify its application to combinatorial problems, discuss historical aspects of the development of the method, and give an overview of some successful applications.
Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems.
Ebook Meta Heuristics Advances And Trends In Local Search Paradigms For Optimization
Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information for a problem. This approach has resulted in a rich arsenal of heuristic algorithms and metaheuristic frameworks for many problems. This chapter discusses the philosophy of the memetic paradigm, lays out the structure of a memetic algorithm, develops several example algorithms, surveys recent work in the field, and discusses the possible future directions of memetic algorithms.
Particle swarm optimization has gained increasing popularity in the past 15 years. Its effectiveness and efficiency has rendered it a valuable metaheuristic approach in various scientific fields where complex optimization problems appear. Its simplicity has made it accessible to the non-expert researchers, while the potential for easy adaptation of operators and integration of new procedures allows its application on a wide variety of problems with diverse characteristics.
Additionally, its inherent decentralized nature allows easy parallelization, taking advantage of modern high-performance computer systems. The present work exposes the basic concepts of particle swarm optimization and presents a number of popular variants that opened new research directions by introducing novel ideas in the original model of the algorithm.
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The focus is placed on presenting the essential information of the algorithms rather than covering all the details. Also, a large number of references and sources is provided for further inquiry. Thus, the present text can serve as a starting point for researchers interested in the development and application of particle swarm optimization and its variants. This chapter presents POPMUSIC, a general decomposition-based framework within the realm of metaheuristics and matheuristics that has been successfully applied to various combinatorial optimization problems.
The basic idea is to optimize subparts of solutions until a local optimum is reached. Implementations of the technique to various problems show its broad applicability and efficiency for tackling especially large-size instances. A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as an array of n random keys, where a random key is a real number, randomly generated, in the continuous interval [0, 1. A decoder maps each array of random keys to a solution of the optimization problem being solved and computes its cost.
The algorithm starts with a population of p arrays of random keys. At each iteration, the arrays are partitioned into two sets, a smaller set of high-valued elite solutions and the remaining nonelite solutions. All elite elements are copied, without change, to the next population. A small number of random-key arrays the mutants are added to the population of the next iteration.
The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong On the virtues of parameterized uniform crossover.