The Bounded Diameter Minimum Spanning Tree problem (BDMST) and the Hop Constrained Minimum Spanning Tree problems (HCMST) are NP-hard combinatorial optimization problems which have their main application in network design. In this thesis an existing relax-and-cut approach for finding lower bounds and approximate solutions to those problems is enhanced and extended, and a hybrid algorithm based ...

This paper concerns efficient parameters tuning (meta-optimization) of a state-of-the-art metaheuristic, Quantum-Inspired Genetic Algorithm (QIGA), in a GPU-based massively parallel computing environment (NVidia CUDAtechnology). A novel approach to parallel implementation of the algorithm has been presented. In a block of threads, each thread transforms a separate quantum individual or differen...

This paper discusses the problem of estimating, on the basis of a given number of say N experiments, the expected performance of a metaheuristic on a class I of benchmark problem instances. The problem of the empirical estimation of the expected behavior of a stochastic optimization algorithm has great relevance both in academic studies and in practical applications. This is particularly true f...

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV thr...

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning ...

Responding to the global call for sustainable renewable energy sources amidst growing demands, exhaustion of fossil fuels, and increasing greenhouse gas emissions, this study introduces a multi-objective optimization an islanded green system. The focus is on implementation sophisticated hybrid metaheuristic approach in Hybrid Renewable Energy System (HRES) specifically designed university campu...

The failure probability of the structures is one of the challenging problems in structural engineering. To obtain the reliability index introduced by Hasofer and Lind, one needs to solve a nonlinear equality constrained optimization problem. In this study, four of the most recent metaheuristic algorithms are utilized for finding the design point and the failure probability of problems with cont...

Heuristic and metaheuristic techniques are used for solving computationally hard optimization problems. Local search is a heuristic technique while Ant colony optimization (ACO), inspired by the ants’ foraging behavior, is one of the most recent metaheuristic technique. These techniques are used for solving optimization problems. Multiple-Input Multiple-Output (MIMO) detection problem is an NP-...