EvoCOMNET Contributions.- New Research in Nature Inspired Algorithms for Mobility Management in GSM Networks.- Adaptive Local Search for a New Military Frequency Hopping Planning Problem.- SS vs PBIL to Solve a Real-World Frequency Assignment Problem in GSM Networks.- Reconstruction of Networks from Their Betweenness Centrality.- A Self-learning Optimization Technique for Topology Design of Computer Networks.- A Comparative Study of Fuzzy Inference Systems, Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection.- EvoFIN Contributions.- Evolutionary Single-Position Automated Trading.- Genetic Programming in Statistical Arbitrage.- Evolutionary System for Generating Investment Strategies.- Horizontal Generalization Properties of Fuzzy Rule-Based Trading Models.- Particle Swarm Optimization for Tackling Continuous Review Inventory Models.- Option Model Calibration Using a Bacterial Foraging Optimization Algorithm.- A SOM and GP Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem.- Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis.- EvoHOT Contributions.- Analysis of Reconfigurable Logic Blocks for Evolvable Digital Architectures.- Analogue Circuit Control through Gene Expression.- Discovering Several Robot Behaviors through Speciation.- Architecture Performance Prediction Using Evolutionary Artificial Neural Networks.- Evolving a Vision-Driven Robot Controller for Real-World Indoor Navigation.- Evolving an Automatic Defect Classification Tool.- Deterministic Test Pattern Generator Design.- An Evolutionary Methodology for Test Generation for Peripheral Cores Via Dynamic FSM Extraction.- Exploiting MOEA to Automatically Geneate Test Programs for Path-Delay Faults in Microprocessors.- EvoIASP Contributions.- Evolutionary Object Detection by Means of Naive Bayes Models Estimation.- An Evolutionary Framework for Colorimetric Characterization of Scanners.- Artificial Creatures for Object Tracking and Segmentation.- Automatic Recognition of Hand Gestures with Differential Evolution.- Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning.- A GA-Based Feature Selection Algorithm for Remote Sensing Images.- An Evolutionary Approach for Ontology Driven Image Interpretation.- Hybrid Genetic Algorithm Based on Gene Fragment Competition for Polyphonic Music Transcription.- Classification of Seafloor Habitats Using Genetic Programming.- Selecting Local Region Descriptors with a Genetic Algorithm for Real-World Place Recognition.- Object Detection Using Neural Networks and Genetic Programming.- Direct 3D Metric Reconstruction from Multiple Views Using Differential Evolution.- Discrete Tomography Reconstruction through a New Memetic Algorithm.- A Fuzzy Hybrid Method for Image Decomposition Problem.- Triangulation Using Differential Evolution.- Fast Multi-template Matching Using a Particle Swarm Optimization Algorithm for PCB Inspection.- EvoMUSART Contributions.- A Generative Representation for the Evolution of Jazz Solos.- Automatic Invention of Fitness Functions with Application to Scene Generation.- Manipulating Artificial Ecosystems.- Evolved Diffusion Limited Aggregation Compositions.- Scaffolding for Interactively Evolving Novel Drum Tracks for Existing Songs.- AtomSwarm: A Framework for Swarm Improvisation.- Using DNA to Generate 3D Organic Art Forms.- Towards Music Fitness Evaluation with the Hierarchical SOM.- Evolutionary Pointillist Modules: Evolving Assemblages of 3D Objects.- An Artificial-Chemistry Approach to Generating Polyphonic Musical Phrases.- Implicit Fitness Functions for Evolving a Drawing Robot.- Free Flight in Parameter Space: A Dynamic Mapping Strategy for Expressive Free Impro.- Modelling Video Games' Landscapes by Means of Genetic Terrain Programming - A New Approach for Improving Users' Experience.- Virtual Constructive Swarm Compositions and Inspirations.- New-Generation Methods in an Interpolating EC Synthesizer Interface.- Composing Music with Neural Networks and Probabilistic Finite-State Machines.- TransFormer #13: Exploration and Adaptation of Evolution Expressed in a Dynamic Sculpture.- EvoNUM Contributions.- Multiobjective Tuning of Robust PID Controllers Using Evolutionary Algorithms.- Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces.- Scalable Continuous Multiobjective Optimization with a Neural Network-Based Estimation of Distribution Algorithm.- Cumulative Step Length Adaptation for Evolution Strategies Using Negative Recombination Weights.- Computing Surrogate Constraints for Multidimensional Knapsack Problems Using Evolution Strategies.- A Critical Assessment of Some Variants of Particle Swarm Optimization.- An Evolutionary Game-Theoretical Approach to Particle Swarm Optimisation.- A Hybrid Particle Swarm Optimization Algorithm for Function Optimization.- EvoSTOC Contributions.- Memory Based on Abstraction for Dynamic Fitness Functions.- A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems.- Compound Particle Swarm Optimization in Dynamic Environments.- An Evolutionary Algorithm for Adaptive Online Services in Dynamic Environment.- EvoTHEORY Contributions.- A Study of Some Implications of the No Free Lunch Theorem.- Negative Slope Coefficient and the Difficulty of Random 3-SAT Instances.- EvoTRANSLOG Contributions.- A Memetic Algorithm for the Team Orienteering Problem.- Decentralized Evolutionary Optimization Approach to the p-Median Problem.- Genetic Computation of Road Network Design and Pricing Stackelberg Games with Multi-class Users.- Constrained Local Search Method for Bus Fleet Scheduling Problem with Multi-depot with Line Change.- Evolutionary System with Precedence Constraints for Ore Harbor Schedule Optimization.

Applications of evolutionary computing: EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings.

https://romisatriawahono.net/lecture/rm/survey/softcomputing/Manjarres%20-%20Harmony%20Search%20Algorithm%20-%202013.pdf

Survey A survey on applications of the harmony search algorithm

In this paper, a novel text clustering method, improved krill herd algorithm with a hybrid function, called MMKHA, is proposed as an efficient clustering way to obtain promising and precise results in this domain. Krill herd is a new swarm-based optimization algorithm that imitates the behavior of a group of live krill. The potential of this algorithm is high because it performs better than other optimization methods; it balances the process of exploration and exploitation by complementing the strength of local nearby searching and global wide-range searching. Text clustering is the process of grouping significant amounts of text documents into coherent clusters in which documents in the same cluster are relevant. For the purpose of the experiments, six versions are thoroughly investigated to determine the best version for solving the text clustering. Eight benchmark text datasets are used for the evaluation process available at the Laboratory of Computational Intelligence (LABIC). Seven evaluation measures are utilized to validate the proposed algorithms, namely, ASDC, accuracy, precision, recall, F-measure, purity, and entropy. The proposed algorithms are compared with the other successful algorithms published in the literature. The results proved that the proposed improved krill herd algorithm with hybrid function achieved almost all the best results for all datasets in comparison with the other comparative algorithms.

Hybrid clustering analysis using improved krill herd algorithm

The harmony search (HS) algorithm is a relatively new population-based metaheuristic optimization algorithm. It imitates the music improvisation process where musicians improvise their instruments' pitch by searching for a perfect state of harmony. Since the emergence of this algorithm in 2001, it attracted many researchers from various fields especially those working on solving optimization problems. Consequently, this algorithm guided researchers to improve on its performance to be in line with the requirements of the applications being developed. These improvements primarily cover two aspects: (1) improvements in terms of parameters setting, and (2) improvements in terms of hybridizing HS components with other metaheuristic algorithms. This paper presents an overview of these aspects, with a goal of providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.

The variants of the harmony search algorithm: an overview

A novel hybridization strategy for krill herd algorithm applied to clustering techniques

Coverage, one of the most important performance metrics for wireless sensor networks, reflects on how well a sensor field is monitored. Coverage problem is a devoted study of a node placement optimization problem in the coverage configuration before network deployment, where the objective is to find the optimal locations to place sensor nodes, such that the number of nodes (or the network cost) can be minimized and the coverage requirements can be satisfied. In this paper, we propose a harmony search (HS)-based deployment algorithm that can locate the optimal number of sensor nodes as well as their optimal locations for maximizing the network coverage and minimizing the network cost. The ability of HS is modified to automatically evolve the appropriate number of sensor nodes as well as their optimal locations. This can be accomplished by integrating the concept of adaptable length encoding in each solution vector to represent a variable number of candidate sensor nodes. Network coverage ratio, number of sensor nodes, and minimum distance between sensor nodes are the chief elements of a new objective function that has been offered to confirm the choice of the optimal number of sensor nodes and their positions. Experimental results show the ability of the proposed algorithm to find the appropriate number of sensor nodes and their locations. Furthermore, a comparative study with a metaheuristic Genetic-based algorithm and a random deployment technique has also been conducted and its results confirm the superiority of the proposed algorithm.

Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm

Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner. In our algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length encoding in each harmony memory vector, this algorithm is able to represent variable numbers of candidate cluster centers at each iteration. A new HS operator, called the “empty operator”, has been introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. Evaluation of the proposed algorithm has been performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results show the ability of this algorithm to find the appropriate number of naturally occurring regions in brain images. Furthermore, the superiority of the proposed algorithm over various state-of-the-art segmentation algorithms is demonstrated quantitatively.

A hybrid harmony search algorithm for MRI brain segmentation

Automatic brain MRI image segmentation is a challenging problem and received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the Harmony Search (HS) hybridized with Fuzzy C-means called DCHS to automatically segment the brain MRI image in an intelligent manner. In this algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length in each harmony memory vector, DCHS is able to encode variable numbers of candidate cluster centers at each iteration. Furthermore, a new HS operator, called the ‘empty operator’ is introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. The proposed algorithm is applied on several simulated T1-weighted normal and MS lesion magnetic resonance brain images. The experimental results show the ability of DCHS to find the appropriate number of naturally occurring regions in brain images. Furthermore, superiority of the proposed algorithm over different clustering-based algorithms is demonstrated quantitatively. All the segmented results obtained by DCHS are also compared with the available ground truth images.

Osama Moh'd Alia

Papers

The variants of the harmony search algorithm: an overview

Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm

A hybrid harmony search algorithm for MRI brain segmentation

A hybrid Harmony Search algorithm to MRI brain segmentation