Results 181 to 190 of about 14,420 (223)
Some of the next articles are maybe not open access.
Metaheuristics: A Canadian Perspective
INFOR: Information Systems and Operational Research, 2008Metaheuristics are generic search strategies that can be adapted to solve complex problems. This paper describes in simple terms the most popular metaheuristics for combinatorial optimization problems. It also emphasizes the main contributions of the Canadian research community in the development and application of metaheuristics.
Michel Gendreau, Jean-Yves Potvin
openaire +1 more source
2008
Decision support systems (DSSs) provide modern solution techniques that help the decision maker to find the best solution to a problem. These embedded solution techniques include and combine, but are not limited to, simulation, exact optimization methods, and heuristics.
openaire +2 more sources
Decision support systems (DSSs) provide modern solution techniques that help the decision maker to find the best solution to a problem. These embedded solution techniques include and combine, but are not limited to, simulation, exact optimization methods, and heuristics.
openaire +2 more sources
A review of metaheuristics in robotics
Computers & Electrical Engineering, 2015Metaheuristics have a substantial history in fine-tuning machine learning algorithms. They gained tremendous popularity in many application domains. Robotics on the other hand is a wide research discipline that embraces artificial intelligence in a complex individually-thinking robot and distributed robots.
Simon Fong 0001 +2 more
openaire +1 more source
Metaheuristics for Dynamic Optimization
2013This book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools
Alba, Enrique +2 more
openaire +3 more sources
Stochastic Search in Metaheuristics
2018Stochastic search is a key mechanism underlying many metaheuristics. The chapter starts with the presentation of a general framework algorithm in the form of a stochastic search process that contains a large variety of familiar metaheuristic techniques as special cases.
Gutjahr, Walter, Montemanni, Roberto
openaire +1 more source
Decomposing Metaheuristic Operations
2013Non-exhaustive local search methods are fundamental tools in applied branches of computing such as operations research, and in other applications of optimisation. These problems have proven stubbornly resistant to attempts to find generic meta-heuristic toolkits that are both expressive and computationally efficient for the large problem spaces ...
Richard Senington, David Duke
openaire +1 more source
Journal of Parallel and Distributed Computing, 2013
El-Ghazali Talbi, Geir Hasle
openaire +1 more source
El-Ghazali Talbi, Geir Hasle
openaire +1 more source
DESICOM as Metaheuristic Search
2020Decomposition into Simple Components (DESICOM) is a constrained matrix factorization method to decompose asymmetric square data matrices and represent them as combinations of very sparse basis matrices as well as dense asymmetric affinity matrices. When cast as a least squares problem, the process of finding the factor matrices needs special attention ...
openaire +1 more source
Metaheuristics for Portfolio Optimization
2017Portfolio optimization refers to allocating an amount of investors’ wealth to different assets in order to satisfy the investors’ preferences for return and risk. We address the portfolio optimization problem with real-world constraints, where traditional optimization methods fail to efficiently find an optimal or near-optional solution.
Sarah El-Bizri, Nashat Mansour
openaire +1 more source