This paper reviews the existing literature on the combination of metaheuristics with machine
learning methods and then introduces the concept of learnheuristics, a novel type of hybrid …

Metaheuristics has attained increasing interest for solving complex real-world problems.
This paper studies the principles and the state-of-the-art of metaheuristic methods for …

The use of surrogate models is a standard method for dealing with complex real-world
optimization problems. The first surrogate models were applied to continuous optimization …

In this paper, an estimation of distribution algorithm (EDA)-based memetic algorithm (MA) is
proposed for solving the distributed assembly permutation flow-shop scheduling problem …

The optimal location and sizing of distributed generation is a suitable option for improving
the operation of electric systems. This paper proposes a parallel implementation of the …

The distributed assembly mixed no-idle permutation flowshop scheduling problem
(DAMNIPFSP), a common occurrence in modern industries like integrated circuit production …

The distributed assembly permutation flow-shop scheduling problem (DAPFSP) is a typical
NP-hard combinatorial optimization problem that has wide applications in advanced …

Evolutionary computation (EC) has gained increasing popularity in dealing with permutation-
based combinatorial optimization problems (PCOPs). Traditionally, EC focuses on solving a …

Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization
technique. It is characterized by the collaborative search in which each particle is attracted …

The aim of this paper is two-fold. First, we introduce a novel general estimation of distribution
algorithm to deal with permutation-based optimization problems. The algorithm is based on …