The goal of the thesis is to study the behavior of the ant colony optimization metaheuristic under uncertain conditions. We choose the probabilistic traveling salesman problem (PTSP) as a test-bed amongst stochastic optimization problems, in much the same way as the traveling salesman problem (TSP) has been considered a standard amongst deterministic optimization problems. Given a set of cities...

In an open electricity, when congestion occurs in a transmission time, it violates system security and system cost is increased. In a deregulated environment particularly, transmission congestion is one of the most prominent technical problems. Thus, while restructuring the power systems one of the important task of ISO/ TSO is to provide congestion free network. Generator rescheduling is the m...

Most optimization problems in real life applications are often highly nonlinear. Local optimization algorithms do not give the desired performance. So, only global optimization algorithms should be used to obtain optimal solutions. This paper introduces a new nature-inspired metaheuristic optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will study HH and validate it again...

Heat transfer search (HTS) is a novel metaheuristic optimization algorithm that simulates the laws of thermodynamics and heat transfer. In this study, the HTS algorithm is adapted to truss structure optimization. Sizing optimization searches for the minimum weight of a structure subject to stress and displacement constraints. Three truss structures often taken as benchmarks in the optimization ...

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a ...

Coordination of multi agent systems remains as a problem since there is no prominent method to completely solve this problem. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems with metaheuristic algorithms. The idea borrowed from swarm intelligence seems working much better than those implementations suggested...

Several constrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In this paper, we evaluate a novel selfadaptive and auto-constructive metaheuristic called Drone Squadron Optimization (DSO) in solving constrained engineering design problems. This paper evaluates DSO with death penalty on three widely tested engineering design pr...

 Metaheuristic optimization has experienced an evolution toward the development of general purpose optimizers. Although the most effective metaheuristics for the solution of hard (in the computational sense) optimization problems are procedures customized to each particular situation, there is an increased interest in developing systems that can effectively operate as general-purpose solvers. ...

Optimal selection of interdependent IT Projects for implementation in multi periods has been challenging in the framework of real option valuation. This paper presents a mathematical optimization model for multi-stage portfolio of IT projects. The model optimizes the value of the portfolio within a given budgetary and sequencing constraints for each period. These sequencing constraints are due ...