Nature-inspired Metaheuristic Method Research Articles

Nature-inspired metaheuristic methods in software testing

Nature-inspired metaheuristic methods in software testing

Nature-inspired metaheuristic optimization algorithms for urban transit routing problem

Designing urban transportation networks is a technological and social reality defined as a decentralized polynomial NP-Hard problem. In order to efficiently resolve this issue, several nature-inspired metaheuristic approaches have been proposed. Although these methods are crucial to resolving urban transportation network challenges, no comprehensive study has been conducted to address these concerns. Thus, this study provides an in-depth examination of nature-inspired metaheuristic methods applied to urban transmission. The current study aims to demonstrate the significant benefits of optimization algorithms in solving the Urban Transit Routing Problem (UTRP).

Improving flexibility in cloud computing using optimal multipurpose particle swarm algorithm with auction rules

ABSTRACT Using exact algorithms in complex environments with multi-objective criteria cannot be considered an appropriate method. For this reason, nature-inspired meta-heuristic methods have been proposed to solve problems such as flexibility in the cloud computing environment. In this research, we use the particle swarm optimisation algorithm and auction approach for two-way load balancing. A cloud computing centre comprises several physical machines called data centres, and data centres consume a lot of energy. To increase efficiency, there is a need to increase flexibility in these systems. The researchers proposed Various methods such as static and dynamic management of virtual machines and automatic processor frequency change systems to reduce energy consumption. In this article, we presented an algorithm based on under-load hosts and particle swarm optimisation and mass movement of virtual machines according to the auction rule to reduce energy as the dynamic management of cloud resources. The proposed method has been evaluated by the cloudsim simulator with PlanetLab real dataset. The experimental results show that, in comparison to the other algorithms, energy consumption has improved and SLA violation has decreased. As well as migration of VMs decreased significantly.

Active control of structures using genetic algorithm with dynamic weighting factors using in the constrained objective function

Active control method by improving specification of well-known intelligent numerical search method i.e. genetic algorithm is developed here. This method reduces displacement of the structure by optimizing the control forces at each time step. The efficiency of the genetic algorithm as a part of nature-inspired metaheuristic methods is highly dependent on the constrained objective function. The constrained objective function is achieved by combining the constraints of the optimization problem. There are several methods to numerically combine these constraints. Using appropriate weighting factors to generate this function has been suggested by many researchers. In previous studies, the selection of these factors has been based on experimental or try and error methods and were constant throughout the control period. Proper selection of weighting factors increases the efficiency of the control method. Presenting a new genetic algorithm method in a way selecting weighting factors dynamically over the structural control period is the aim of this paper. Here, weighting factors are non-static in nature and are dynamically selected at each time step according to the memory of the previous step. Numerical results clearly prove the accuracy and efficiency of the proposed control process in comparison with Constant weighting factors methods.

A Comparative Analysis of Three Computational-Intelligence Metaheuristic Methods for the Optimization of TDEM Data

We focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a plethora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics.

A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem

Meta-heuristics are problem-independent optimization techniques which provide an optimal solution by exploring and exploiting the entire search space iteratively. These techniques have been successfully engaged to solve distinct real-life and multidisciplinary problems. A good amount of literature has been already published on the design and role of various meta-heuristic algorithms and on their variants. The aim of this study is to present a comprehensive analysis of nature-inspired meta-heuristic utilized in the domain of feature selection. A systematic review methodology has been used for synthesis and analysis of one hundered and seventy six articles. It is one of the important multidisciplinary research areas that assist in finding an optimal set of features so that a better rate of classification can be achieved. The concept of feature selection process along with relevance and redundancy metric is briefly elucidated. A categorical list of nature-inspired meta-heuristic techniques has been presented. The major applications of these techniques are explored to highlight the least and most explored areas. The area of disease diagnosis has been extensively assessed. In addition, the special attention has been given on highlighting the role and performance of binary and chaotic variants of different nature-inspired meta-heuristic techniques. The summary of nature-inspired meta-heuristic methods and their variants along with datasets, performance (mean, best, worst, error rate and standard deviation) is also depicted. In addition, the detailed publication trend of meta-heuristic feature selection approaches has also been presented. The research gaps have been identified for the researcher who inclines to design or analyze the performance of divergent meta-heuristic techniques in solving feature selection problem.

Ant colony algorithm as a high-performance method in resource estimation using LVA field; A case study: Choghart Iron ore deposit

Kriging is an advanced geostatistical procedure that generates an estimated surface or 3D model from a scattered set of points. This method can be used for estimating resources using a grid of sampled boreholes. However, conventional ordinary kriging (OK) is unable to take locally varying anisotropy (LVA) into account. A numerical approach has been presented that generates an LVA field by calculating the anisotropy parameters (direction and magnitude) in each cell of the estimation grid. After converting the shortest anisotropic distances to Euclidean distances in the grid, they can be used in variography and kriging equations (LVAOK). The ant colony optimization (ACO) algorithm is a nature-inspired metaheuristic method that is applied to extract image features. A program has been developed based on the application of ACO algorithm, in which the ants choose their paths based on the LVA parameters and act as a moving average window on a primary interpolated grid. If the initial parameters of the ACO algorithm are properly set, the ants would be able to simulate the mineralization paths along continuities. In this research work, Choghart iron ore deposit with 2,447 composite borehole samples was studied with LVA-kriging and ACO algorithm. The outputs were cross-validated with the 111,131 blast hole samples and the Jenson-Shannon (JS) criterion. The obtained results show that the ACO algorithm outperforms both LVAOK and OK (with a correlation coefficient value of 0.65 and a JS value of 0.025). Setting the parameters by trial-and-error is the main problem of the ACO algorithm.

The Nature-Inspired Metaheuristic Method for Predicting the Creep Strain of Green Concrete Containing Ground Granulated Blast Furnace Slag

The aim of this study was to develop a nature-inspired metaheuristic method to predict the creep strain of green concrete containing ground granulated blast furnace slag (GGBFS) using an artificial neural network (ANN)model. The firefly algorithm (FA) was used to optimize the weights in the ANN. For this purpose, the cement content, GGBFS content, water-to-binder ratio, fine aggregate content, coarse aggregate content, slump, the compaction factor of concrete and the age after loading were used as the input parameters, and in turn, the creep strain (εcr) of the GGBFS concrete was considered as the output parameters. To evaluate the accuracy of the FA-ANN model, it was compared with the well-known genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO). Results indicated that the ANNs model, in which the weights were optimized by the FA, were more capable, flexible and precise than other optimization algorithms in predicting the εcr of GGBFS concrete.

A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection

Due to the widespread use of the internet, computer systems are prone to information theft that has led to the emergence of Intrusion Detection Systems (IDSs). Various approaches such as machine learning, Bayesian-based algorithms, nature-inspired metaheuristic methods, swarm intelligent algorithms, and Markov neural networks have proposed to choose effective and efficacious features and improve the performance of intrusion detection systems. In this paper, we propose a new hybrid classification method based on Artificial Bee Colony (ABC) and Artificial Fish Swarm (AFS) algorithms. The Fuzzy C-Means Clustering (FCM) and Correlation-based Feature Selection (CFS) techniques are applied to divide the training dataset and remove the irrelevant features, respectively. In addition, If-Then rules are generated through the CART technique according to the selected features in order to distinguish the normal and anomaly records. Likewise, the proposed hybrid method is trained via the generated rules. The simulation results on NSL-KDD and UNSW-NB15 datasets demonstrate that the proposed method outperforms in terms of performance metrics and can achieve 99% detection rate and 0.01% false positive rate. In addition, analysis of computational complexity and time cost illustrate that overhead of the proposed method is comparable with counterpart approaches.

The bee colony optimization algorithm and its convergence

The bee colony optimization BCO algorithm is a nature-inspired meta-heuristic method for dealing with hard, real-life combinatorial and continuous optimisation problems. It is based on the foraging habits of honeybees and was proposed by Lucic and Teodorovic in 2001. BCO is a simple, but effective meta-heuristic method that has already been successfully applied to various combinatorial optimisation problems in transport, location analysis, scheduling and some other fields. This paper provides theoretical verification of the BCO algorithm by proving some convergence properties. As a result, the gap between successful practice and missing theory is reduced.

Bee Colony Optimization - part I: The algorithm overview

This paper is an extensive survey of the Bee Colony Optimization (BCO) algorithm, proposed for the first time in 2001. BCO and its numerous variants belong to a class of nature-inspired meta-heuristic methods, based on the foraging habits of honeybees. Our main goal is to promote it among the wide operations research community. BCO is a simple, but efficient meta-heuristic technique that has been successfully applied to many optimization problems, mostly in transport, location and scheduling fields. Firstly, we shall give a brief overview of the other meta-heuristics inspired by bees? foraging principles pointing out the differences between them. Then, we shall provide the detailed description of the BCO algorithm and its modifications, including the strategies for BCO parallelization, and giving the preliminary results regarding its convergence. The application survey is elaborated in Part II of our paper.