Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of solutions, called the Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an a...

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee...

Bridging the gap between simulation and reality for successful micro-grid (MG) implementation requires accurate mathematical modelling of underlying energy infrastructure extensive optimisation design space defined by all possible combinations size equipment. While exact approaches to MG capacity planning are highly computationally efficient, they often fail preserve associated problem nonlinea...

In the last few decades, several effective algorithms for solving the resource-constrained project scheduling problem have been proposed. However, the challenging nature of this problem, summarised in its strongly NP-hard status, restricts the effectiveness of exact optimisation to relatively small instances. In this paper, we present a new meta-heuristic for this problem, able to provide near-...

For some years now, Meta-Heuristic methods have demonstrated their ability to tackle large-scale optimisation problems. Up to now, several frameworks have been implemented for this family of methods. Some of them are either dedicated to Local Search such as EasyLocal++[5], Localizer[8], LocalSearch framework[1], Templar[7], HotFrame[4] or to Evolutionary Computation such as EOS[2], EASEA[3]. Th...

ABSTRACT Optimization algorithms are normally influenced by metaheuristic approach. In recent years several hybrid methods for optimization are developed to find out a better solution. The proposed work using meta-heuristic Nature Inspired algorithm is applied with back-propagation method to train a feedforward neural network. Firefly algorithm is a nature inspired meta-heuristic algorithm, and...

The join query optimization problem has been widely addressed in relational database management systems (RDBMS). The problem consists of finding a join order that minimizes the time required to execute a query. Many strategies have been implemented to solve this problem including deterministic algorithms, randomized algorithms, meta-heuristic algorithms and hybrid approaches. Such methodologies...

The vehicle routing problem (VRP) is one of the most important combinational optimization problems that has nowadays received much attention because of its real application in industrial and service problems. The VRP involves routing a fleet of vehicles, each of them visiting a set of nodes such that every node is visited by exactly one vehicle only once. So, the objective is to minimize the to...

We evaluate three different approaches to road network partitioning for simulation. The first approach consists of algorithms that rely on dividing the road physical network spatially into rectangular blocks of different sizes. Our second approach uses a graph representation of the road network, and uses meta-heuristic search algorithms to partition the graph. The final hybrid scheme builds clu...

Expeditious modelling of virtual urban environments consists of generating realistic 3d models from limited information. It has several practical applications but typically suffers from a lack of accuracy in the parameter values that feed the modeller. By gathering small amounts of information about certain key urban areas, it becomes possible to feed a system that automatically compares and ad...