This paper presents the design of two Ant Colony Optimization (ACO) approaches and their improved variants on the degree-constrained minimum spanning tree (d-MST) problem. The first approach, which we call p-ACO, uses the vertices of the construction graph as solution components, and is motivated by the well-known Prim’s algorithm for constructing MST. The second approach, known as k-ACO, uses ...

Based on the electromagnetism-like algorithm (EM), we propose a novel hybrid learning algorithms which is the improved EM algorithm with genetic algorithm technique (IEMGA) for recurrent fuzzy neural system design. IEMGA are composed of initialization, local search, total force calculation, movement, and evaluation. They are hybridization of EM and GA. EM algorithm is a population-based meta-he...

In this paper, we present a new algorithm, which is called electromagnetism meta-heuristic restarted Arnoldi algorithm (EM-RA) for calculating eigenvalues and eigenvectors of a non-symmetric matrix. In most of the restarting methods the basic idea is a selection of the best initial eigenvector, but our aim is to improve the initial eigenvectors in each iteration of the restarting method. Numeri...

We consider the 3-stage two-dimensional bin packing problem, which occurs in real-world problems such as glass cutting. For it, we present a new integer linear programming formulation and a branch and price algorithm. Column generation is performed by applying either a greedy heuristic or an Evolutionary Algorithm (EA). Computational experiments show the benefits of the EA-based approach. The h...

Increasing economy’s resistance against the menace of sanctions, various risks, shocks, and internal and external threats are one of the main national policies which can be implemented through bank investments. Investment project selection is a complex and multi-criteria decision-making process that is influenced by multiple and often some conflicting objectives. This paper studies portfolio inve...

This paper presents a numerical study of a stochastic augmented Lagrangian algorithm to solve continuous constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is...

Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Multi Expression Programming (MEP) technique is use...

We investigate three approaches to Boolean satissability problems. We study and compare the best heuristic algorithm WGSAT and two evolutionary algorithms, an evolution strategy and an evolutionary algorithm adapting its own tness function while running. The results show that the adaptive EA outperforms the other two approaches. The power of this EA originates from the adaptive mechanism, which...