One of the essential aspects of power system planning is generation expansion planning (GEP). The purpose of GEP is to enhance construction planning and reduce the costs of installing different types of power plants. This paper proposes a method based on Genetic Algorithm (GA) for GEP in the...

This study presents a novel approach for wind farm micro-siting in complex terrain based on an improved Genetic Algorithm (GA). The proposed approach is used for optimizing the layout of wind turbines in a real terrain in Changsha, China. Firstly, a combination of computational fluid dynamics...

Portfolio selection problem is a major subject in finance where investors deal with selecting satisfying portfolio which is composed of a vast number of risky assets, under some restricting criteria that are defined by themselves. Asset prices can be effected from different events, such as...

We present a new interactive procedure for evolutionary multiobjective optimization involving the decision rule preference model in the search for the best compromise solution. As usual in the interactive multiobjective optimization, preference elicitation phases alternate with optimization...

Portfolio optimisation for a Fund of Hedge Funds (“FoHF”) has to address the asymmetric, non-Gaussian nature of the underlying returns distributions. Furthermore, the objective functions and constraints are not necessarily convex or even smooth. Therefore traditional portfolio optimisation...

This paper discusses the role that Genetic Algorithms (GA) can have in determining asset allocation for multi sector funds. We present an asset allocation model where the investors' utility function departs from the quadratic utility function assumed by the standard Mean-Variance optimisation....

Policy makers constantly face optimal control problems: what controls allow to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear- quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this...

Combinatorial optimization problems are usually NP-hard and the solution space of them is very large. Therefore the set of feasible solutions cannot be evaluated one by one. Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are metaheuristic techniques...

Policy makers constantly face optimal control problems: what controls allow to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear-quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this...