Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired b...

A metaheuristic is generally a procedure designed to find a good solution to a difficult optimization problem. Known optimization search metaheuristics heavily rely on parameters, which are usually introduced so that the metaheuristic follows some supposedly related to the optimization problem natural process (simulated annealing, swarm optimization, genetic algorithms). Adjusting the parameter...

In this paper, we propose a multi-objective evolutionary metaheuristic approach based on the Pareto Ant Colony Optimization (P-ACO) and non-dominated genetic sorting algorithms (NSGA II NSGA III) to solve bi-objective portfolio optimization problem. P-ACO is used select best assets composing efficient portfolio. Then, III are separately find proportional weights of budget allocated selected The...

The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristi...

We consider the application of a new metaheuristic technique that we have proposed, known as PROBE (Population Reinforced Optimization Based Exploration), to the multiconstraint knapsack problem (MKP). In the current section, we begin by giving a brief introduction to PROBE; we then briefly review other metaheuristic approaches for the MKP. In Section 2, we describe our PROBE approach for the M...

The incorporation of data mining techniques into metaheuristics has been efficiently adopted to solve several optimization problems. Nevertheless, we observe in the literature that this hybridization has been limited to problems in which the solutions are characterized by sets of (unordered) elements. In this work, we develop a hybrid data mining metaheuristic to solve a problem for which solut...

This work presents a hybrid optimization framework for tackling cutting and packing problems, which is based upon a particular combination scheme between heuristic and exact methods. A metaheuristic engine works as a generator of reduced instances for the original optimization problem, which are formulated as mathematical programming models. These instances, in turn, are solved by an exact opti...

the present study is concerned with optimal shape determination of inhomogeneous and temperature dependent domains under steady state heat conduction. such situations are important in many thermal design problems, especially in shape design of electronic components and chips. in the present paper, we formulate the shape optimization problem based on volume minimization of heat conductive materi...

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineer...