An improved penalty algorithm using model order reduction for MIPDECO problems with partial observations

An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems

Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and ...

Model Checking with the Partial Order Reduction

Partial-Order Reduction for GPU Model Checking

Model checking using GPUs has seen increased popularity over the last years. Because GPUs have a limited amount of memory, only small to medium-sized systems can be verified. For on-the-fly explicitstate model checking, we improve memory efficiency by applying partialorder reduction. We propose novel parallel algorithms for three practical approaches to partial-order reduction. Correctness of t...

Improved Pade-Pole Clustering Approach using Genetic Algorithm for Model Order Reduction

Reducing the order of higher order systems by mixed approach is Improved Pade-Pole clustering based method to derive a reduced order approximation for a stable continuous time system is presented. In this method, the denominator polynomial of the reduced order model is derive by improved pole-clustering approach and the numerator polynomial are obtain through Padé approximation technique and by...

An Algorithm for Two Dimensional Cutting Stock Problems with Demand

In this paper, two-dimensional cutting stock problem with demand has been studied.In this problem, cutting of large rectangular sheets into specific small pieces should be carried out hence, the waste will be minimized. Solving this problem is important to decrease waste materials in any industry that requires cutting of sheets. In most previus studies, the demand of pieces has not been usually...