J. Del Ser

Survey A survey on applications of the harmony search algorithm

This paper thoroughly reviews and analyzes the main characteristics and application portfolio of the so-called Harmony Search algorithm, a meta-heuristic approach that has been shown to achieve excellent results in a wide range of optimization problems. As evidenced by a number of studies, this algorithm features several innovative aspects in its operational procedure that foster its utilization in diverse fields such as construction, engineering, robotics, telecommunications, health and energy. This manuscript will go through the most recent literature on the application of Harmony Search to the aforementioned disciplines towards a three-fold goal: (1) to underline the good behavior of this modern meta-heuristic based on the upsurge of related contributions reported to date; (2) to set a bibliographic basis for future research trends focused on its applicability to other areas; (3) to provide an insightful analysis of future research lines gravitating on this meta-heuristic solver.

A new grouping genetic algorithm for clustering problems

Highlights? A hybrid grouping-encoding algorithm for clustering problems is presented. ? Details on the encoding, operators and parallelization are given. ? Results in synthetic and real clustering problems are provided. In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithms performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology.

A multi-objective grouping Harmony Search algorithm for the optimal distribution of 24-hour medical emergency units

Highlights? A multi-objective Harmony Search algorithm is presented. ? A problem of 24-h emergency centers location is solved. ? A realistic case in two regions of Spain if discussed. This paper presents a novel multi-objective heuristic approach for the efficient distribution of 24-h emergency units. This paradigm is essentially a facility location problem that involves determining the optimum locations, within the existing health care centers, where to deploy 24-h emergency resources, as well as an efficient assignment of patients to such newly placed resources through the existing medical care infrastructure. The formulation of the underlying NP-complete problem is based on a bi-objective distance and cost metric, which is tackled in our approach by combining a Harmony Search algorithm with a grouping encoding and a non-dominated solution sorting strategy. Additionally, the nominal grouping encoding procedure has been redefined in order to reduce the dimension of the search space, thus allowing for a higher efficiency of the searching process. Extensive simulations in a real scenario - based on the geographic location of medical centers over the provinces of Guadalajara and Cuenca (Spain) - show that the proposed algorithm is statistically robust and provides a wide range of feasible solutions, hence offering multiple alternatives for the distribution of emergency units.

A comparative study of two hybrid grouping evolutionary techniques for the capacitated P-median problem

This paper addresses the application of two different grouping-based algorithms to the so-called capacitated P-median problem (CPMP). The CPMP is an NP-complete problem, well-known in the operations research field, arising from a wide spectrum of applications in diverse fields such as telecommunications, manufacturing and industrial engineering. The CPMP problem has been previously tackled by using distinct algorithmic approaches, among which we focus on evolutionary computation techniques. The work presented herein elaborates on these evolutionary computation algorithms when applied to the CPMP, by evaluating the performance of a novel grouping genetic algorithm (GGA) and a novel grouping harmony search approach (GHS). Both GGA and GHS are hybridized with a specially tailored local search procedure for enhancing the overall performance of the algorithm in the particular CPMP scenario under consideration. This manuscript delves into the main characteristics of the proposed GGA and GHS schemes by thoroughly describing the grouping encoding procedure, the evolutionary operators (GGA) and the improvisation process (GHS), the aforementioned local search procedure and a repairing technique that accounts for the feasibility of the solutions iteratively provided by both algorithms. The performance of the proposed algorithms is compared with that of several existing evolutionary-based algorithms for CPMP instances of varying size, based on which it is concluded that GGA and GHS dominate any other approaches published so far in the literature, specially when the size of the CPMP increases. The experimental section of the paper tries to evaluate the goodness of the grouping encoding, and also the differences in behavior between the GGA and GHS due to the meta-heuristic algorithm used.

A novel grouping harmony search algorithm for the multiple-type access node location problem

In this paper we present a novel grouping harmony search algorithm for the Access Node Location Problem (ANLP) with different types of concentrators. The ANLP is a NP-hard problem where a set of distributed terminals, with distinct rate demands, must be assigned to a variable number of concentrators subject to capacity constraints. We consider the possibility of choosing between different concentrator models is given in order to provide service demand at different cost. The ANLP is relevant in communication networks design, and has been considered before within the design of MPLS networks, for example. The approach we propose to tackle the ANLP problem consists of a hybrid Grouping Harmony Search (GHS) algorithm with a local search method and a technique for repairing unfeasible solutions. Moreover, the presented scheme also includes the adaptation of the GHS to a differential scheme, where each proposed harmony is obtained from the same harmony in the previous iteration. This differential scheme is perfectly adapted to the specifications of the ANLP problem, as it utilizes the grouping concept based on the proximity between nodes, instead of being only based on the grouping concept. This allows for a higher efficiency on the searching process of the algorithm. Extensive Monte Carlo simulations in synthetic instances show that this proposal provides faster convergence rate, less computational complexity and better statistical performance than alternative algorithms for the ANLP, such as grouping genetic algorithms, specially when the size of the scenario increases. We also include practical results for the application of GHS to a real wireless network deployment problem in Bizkaia, northern Spain.

Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search—Extreme Learning Machine Approach

The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques.

Efficient citywide planning of open WiFi access networks using novel grouping harmony searchheuristics

This paper proposes the application of a novel meta-heuristic algorithm to the metropolitan wireless local area network deployment problem. In this problem, the coverage level of the deployed network must be maximized while meeting an assigned maximum budget, set beforehand. Specifically, we propose an approach based on the Harmony Search (HS) algorithm, with three main technical contributions: (1)the adaptation of the HS algorithm to a grouping scheme; (2)the adaptation of the improvisation operators driving the algorithm to the specific characteristics of the optimization problem to be tackled; and (3)its performance assessment via a simulated experiment inspired by real statistics in the city of Bilbao (Basque Country, northern Spain). Moreover, a comparison study of the proposed algorithm with a previous published grouping genetic algorithm is carried out, to further validate its performance. In light of the simulation results obtained from extensive experiments and several complexity considerations, we conclude that the proposed algorithm outperforms its genetically inspired counterpart, not only in terms of computation time, but also in the coverage level of the solution obtained.

A Coral Reefs Optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion

Graphical abstractDisplay Omitted HighlightsA Mobile Network Deployment Problem is tackled using the CRO.The Coral Reefs Optimization is based on corals and corals reefs biology.A full description of the new algorithm is carried out.Experimental comparison with alternative soft-computing algorithms is done. In this paper we apply a novel meta-heuristic approach, the Coral Reefs Optimization (CRO) algorithm, to solve a Mobile Network Deployment Problem (MNDP), in which the control of the electromagnetic pollution plays an important role. The CRO is a new bio-inspired meta-heuristic algorithm based on the growing and evolution of coral reefs. The aim of this paper is therefore twofold: first of all, we study the performance of the CRO approach in a real hard optimization problem, and second, we solve an important problem in the field of telecommunications, including the minimization of electromagnetic pollution as a key concept in the problem. We show that the CRO is able to obtain excellent solutions to the MNDP in a real instance in Alcala de Henares (Madrid, Spain), improving the results obtained by alternative algorithms such as Evolutionary, Particle Swarm Optimization or Harmony Search algorithms.

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems

This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases.