Two algorithms for global optimization

Physics Based Metaheuristic Algorithms for Global Optimization

In recent years, several optimization methods especially metaheuristic optimization methods have been developed by scientists. People have utilized power of nature to solve problems. Therefore, those metaheuristic methods have imitated physical and biological processes of nature. In 2007, Big Bang Big Crunch optimization algorithm based on evolution of universe and in 2009, Gravitational Search...

Hybridizing local search algorithms for global optimization

In this paper, we combine two types of local search algorithms for global optimization of continuous functions. In the literature, most of the hybrid algorithms are produced by combination of a global optimization algorithm with a local search algorithm and the local search is used to improve the solution quality, not to explore the search space to find independently the global optimum. The foc...

Global Optimization Algorithms for Financial Planning Problems

It is becoming apparent that convex financial planning models are at times a poor approximation of the real world. More realistic, and more relevant models need to dispense with normality assumptions, and concavity of the utility functions to be optimized. Moreover, the problems are large scale but structured, consequently specialized algorithms have been proposed for their solution. The aim of...

Global optimization algorithms for bound constrained problems

Parallel algorithms for global optimization problems

In this chapter we discuss parallel algorithms for solving some classes of global optimization problems. We present an introductory survey of parallel algorithms that have been used to solve structured problems (partially separable, and large-scale block structured problems), algorithms based on parallel local searches, Monte Carlo approaches and parallel algorithms for some location problems.