Abstract
This paper describes a novel algorithm for numerical optimization, which we call Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. Our algorithm has a close resemblance to local optimization heuristics such as random walk, gradient descent and, hill-climbing. However, SAC algorithm is capable of performing global optimization efficiently in any kind of space. Tested on 15 well-known unconstrained optimization problems, it confirmed that SAC is competitive against representative state-of-the-art approaches.KeywordsParticle Swarm OptimizationGlobal OptimizationEvolutionary ComputationNumerical OptimizationMemetic AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
