Solution of Abstract Optimization problems with two or more conflicting functions or objectives by using metaheuristics has attracted attention of researches and become a rapidly developing area known as Multiobjective Optimization. Metaheuristics are non-exact techniques aimed to produce satisfactory solutions to complex optimization problems where exact techniques are not applicable; they are...

Nature-inspired metaheuristics for optimization have proven successful, due to their fine balance between exploration and exploitation of a search space. This balance can be further refined by hybridization. In this paper, we conduct experiments with some of the most promising nature-inspired metaheuristics, for assessing their performance when using them to replace backpropagation as a learnin...

A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms (Sörensen and Glover, To appear). Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist. A problem-specific implementati...

This paper provides an overview of Software Project Scheduling problem as a combinatorial optimization problem. Since its inception by Alba, there have been multiple models to solve this problem. Metaheuristics provide high-level strategies capable of solving these problems efficiently. A set of metaheuristics used to solve this problem is presented, showing the resolution structure and its app...

Several constrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In this paper, we evaluate a novel selfadaptive and auto-constructive metaheuristic called Drone Squadron Optimization (DSO) in solving constrained engineering design problems. This paper evaluates DSO with death penalty on three widely tested engineering design pr...

⎯ Optimization of complex systems has been for many years limited to problems that could be formulated as mathematical programming models of linear, nonlinear and integer types. Problemspecific heuristics that do not require a mathematical formulation of the problem have been also used for optimizing complex systems, however, they generally must be developed in a case-by-case basis. Recent rese...

This article is about a tutorial on hybrid metaheuristics which was given at the first edition of the conference Theory and Practice of Natural Computing, held in October 2012 in Tarragona, Spain. Hybrid metaheuristics are techniques for (combinatorial) optimization that result from a combination of algorithmic components originating from different optimization methods. The tutorial covers five...

A multi-antenna aided base station (BS) supporting several downlink users with the aid of a reconfigurable intelligent surface (RIS) programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by set sparse discrete values are used for reasons cost-efficiency. Our challenging objective to jointly design beamformers at BS and RIS’s improving throughput all maximizing t...

In the optimization of engineering designs, traditional search and optimization methods face at least two diiculties: (i) since each is specialized in solving a particular type of problem, one method does not work well on diierent types of problems (ii) most of them are designed to work on continuous search spaces. Since diierent optimal engineering design problems give rise to objective and co...

Modifications that introduce parallelization of standard cuckoo search algorithm are proposed in this paper. Basic form of the cuckoo search algorithm has already shown great potential for optimization problems, especially when applied to unconstrained continuous functions. In this paper two aspects of parallelization are proposed. The first one addresses the performance issue, while the second...