Open Access
Variable Neighborhood Search.
Pierre Hansen,Nenad Mladenović +1 more
- pp 759-787
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The article was published on 2018-01-01 and is currently open access. It has received 3022 citations till now. The article focuses on the topics: Variable neighborhood search.read more
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Salp Swarm Algorithm
Seyedali Mirjalili,Amir H. Gandomi,Seyedeh Zahra Mirjalili,Shahrzad Saremi,Hossam Faris,Seyed Mohammad Mirjalili +5 more
TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
Book
Metaheuristics: From Design to Implementation
TL;DR: This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling.
Journal ArticleDOI
Variable neighborhood search: Principles and applications
Pierre Hansen,Nenad Mladenović +1 more
TL;DR: In this article, a simple and effective metaheuristic for combinatorial and global optimization, called variable neighborhood search (VNS), is presented, which can easily be implemented using any local search algorithm as a subroutine.
Journal ArticleDOI
An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows
Stefan Ropke,David Pisinger +1 more
TL;DR: This paper presents a heuristic for the pickup and delivery problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows that is very robust and is able to adapt to various instance characteristics.
Greedy Randomized Adaptive Search Procedures.
TL;DR: This paper defines the various components comprising a GRASP and demonstrates, step by step, how to develop such heuristics for combinatorial optimization problems.
References
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Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.