Numerical methods for optimal control of hybrid dynamical systems are considered where the discrete dynamics and the nonlinear continuous dynamics are tightly coupled. A decomposition approach for numerically solving general mixed-integer continuous optimal control problems (MIOCPs) is discussed. In the outer optimization loop a branch-and-bound binary tree search is used for the discrete varia...

Reactive power optimization is important to ensure power quality, improve system security, and reduce active power loss. So, this paper proposed parallel immune particle swarm optimization (PIPSO) algorithm. This algorithm makes basic particle swarm optimization (BPSO) and discrete particle swarm optimization (DPSO) to optimize in parallel, and improves the convergence capability of particle sw...

Robot footstep planning strategies can be divided in two main approaches: discrete searches and continuous optimizations. While discrete searches have been broadly applied, continuous optimizations approaches have been restricted for humanoid platforms. This article introduces a generalized continuous-optimization approach for multilegged footstep planning which can be adapted to different plat...

Many practical and complex problems in industry and business such as the routing problems, scheduling, networks design, telephone routing etc.,. are in the class of intractable combinatorial (discrete) or numerical (continuous or mixed) optimization problems. Many traditional methods were developed for solving continuous optimization problems, while discrete problems are being solved using heur...

Metaheuristics have been established as one of the most practical approach to simulation optimization. However, these methods are generally designed for combinatorial optimization, and their implementations do not always adequately account for the presence of simulation noise. Research in simulation optimization, on the other hand, has focused on convergent algorithms, giving rise to the impres...

This editorial note presents the motivations, objectives, and structure of the special issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. In addition, it provides the link to an associated Website where complementary material to the special issue is available.

Metaheuristics are often used to find solutions real and complex problems. These algorithms can solve optimization problems provide close the global optimum in an acceptable reasonable time. In this paper, we will present a new bio-inspired metaheuristic based on natural chasing attacking behaviors of rats nature, called Rat swarm optimizer. Which has given good results solving several continuo...

A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amount of computer run time consumption. In this st...

We develop new Markov chain Monte Carlo samplers for neighborhood generation in global optimization algorithms based on Hit-and-Run. The success of Hit-and-Run as a sampler on continuous domains motivated Discrete Hit-and-Run with random biwalk for discrete domains. However, the potential in efficiencies in the implementation, which requires a randomization at each move to create the biwalk, le...

In this research we present new results on discrete Lagrangian methods (DLM) and extend our previous (incomplete and highly simpli ed) theory on the method. Our proposed method forms a strong mathematical foundation for solving general nonlinear discrete optimization problems. Speci cally, we show for continuous Lagrangian methods the relationship among local minimal solutions satisfying constr...