This paper addresses the solution of bound-constrained optimization problems using
algorithms that require only the availability of objective function values but no derivative …

Heuristic optimization algorithms seek good feasible solutions to optimization problems in
circumstances where the complexities of the problem or the limited time available for …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving
systems with sophisticated collective intelligence. Composed of simple interacting agents …

We present a new algorithm for finding the global minimum of a multivariate function subject
to simple bounds. The algorithm is a modification of the standard Lipschitzian approach that …

Changes and additions are sprinkled throughout. Among the significant new features are:•
Markov-chain simulation (Sections 1. 3, 2. 6, 3. 6, 4. 3, 5. 4. 5, and 5. 5);• gradient estimation …

In science, engineering and economics, decision problems are frequently modelled by
optimizing the value of a (primary) objective function under stated feasibility constraints. In …

Inspired by a method by Jones et al.(1993), we present a global optimization algorithm
based on multilevel coordinate search. It is guaranteed to converge if the function is …

In this stochastic approach to global optimization, clustering techniques are applied to
identify local minima of a real valued objective function that are potentially global. Three …

This survey covers the state of the art of techniques for solving general-purpose constrained
global optimization problems and continuous constraint satisfaction problems, with …