Metaheuristics is basically a higher level procedure, which generates a simpler procedure to solve an optimization problem. Optimization is the process of adjusting the inputs to or characteristics of a device, mathematical process, or experiment to find the minimum or maximum output or result. The input consists of variables; the process or function is known as the cost function, objective fun...

This work deals with the application of metaheuristics to the fuel consumption minimization problem of hybrid electric vehicles (HEV) considering exactly specified driving cycles. A genetic algorithm, a downhill-simplex method and an algorithm based on swarm intelligence are used to find appropriate parameter values aiming at fuel consumption minimization. Finally, the individual metaheuristics...

Facility layout planning (FLP) involves a set of design problems related to the arrangement elements that shape industrial production systems in physical space. The fact they are considered one most important decisions as part business operation strategies, and their proven repercussion on systems’ costs, efficiency productivity, mean this theme has been widely addressed science. In context, pr...

Memetic algorithms are population-based metaheuristics aimed to solve hard optimization problems. These techniques are explicitly concerned with exploiting available knowledge in order to achieve the most effective resolution of the target problem. The rationale behind this optimization philosophy, namely the intrinsic theoretical limitations of problem-unaware optimization techniques, is prese...

Derivative-free methods are being explored recently due to the increased complexity of the models used in the optimization problems, and the impossibility/inconvenience of using derivatives in several situations. However, those methods show some limitations due to their low convergence rate, and when the problem is high-dimensional. Metaheuristics are another commonly adopted type of search tec...

The Portfolio selection problem is a relevant problem arising in finance and economics. Some practical formulations of the problem include various kinds of nonlinear constraints and objectives and can be efficiently solved by approximate algorithms. Among the most effective approximate algorithms, are metaheuristic methods that have been proved to be very successful in many applications. This ...

This paper introduces DAFO, a Distributed Agent Framework for Optimization that helps in designing and applying Coevolutionary Genetic Algorithms (CGAs). CGAs have already proven to be efficient in solving hard optimization problems, however they have not been considered in the existing agent-based metaheuristics frameworks that currently provide limited organization models. As a solution, DAFO...

This paper addresses the comparison of heuristic algorithms in the case of real functions optimization and knapsack problem. Metaheuristics for algorithms hill climbing, simulated annealing, tabu search and genetic algorithm are shown, test results are presented and conclusions are drawn. Input parameters for optimization functions problem are optimised on a sample of functions. Also, algorithm...

We present a hybrid search technique based on metaheuristics for approximately solving the vehicle routing problem with time windows (VRPTW). The approach is two phased; a global customer clustering phase based on genetic algorithms (GAs) and a post-optimization local search technique based on tabu search (TS). We also devise a new crossover operator for the VRPTW and compare its performance wi...

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem...