Efficient table-free sampling methods for the exponential, Cauchy, and normal distributions
Communications of the ACM
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
SIAM Journal on Optimization
A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search
Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search
Journal of Global Optimization
Minimizing the multimodal functions with Ant Colony Optimization approach
Expert Systems with Applications: An International Journal
A restarted and modified simplex search for unconstrained optimization
Computers and Operations Research
A Heuristic for Nonlinear Global Optimization
INFORMS Journal on Computing
Handbook of Metaheuristics
A survey on optimization metaheuristics
Information Sciences: an International Journal
A nested heuristic for parameter tuning in Support Vector Machines
Computers and Operations Research
Two metaheuristic approaches for solving multidimensional two-way number partitioning problem
Computers and Operations Research
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Variable Neighborhood Search (VNS) has shown to be a powerful tool for solving both discrete and box-constrained continuous optimization problems. In this note we extend the methodology by allowing also to address unconstrained continuous optimization problems. Instead of perturbing the incumbent solution by randomly generating a trial point in a ball of a given metric, we propose to perturb the incumbent solution by adding some noise, following a Gaussian distribution. This way of generating new trial points allows one to give, in a simple and intuitive way, preference to some directions in the search space, or, contrarily, to treat uniformly all directions. Computational results show some advantages of this new approach.