Proposed Fitness Function Research Articles

A secure routing approach based on league championship algorithm for wireless body sensor networks in healthcare.

Patients must always communicate with their doctor for checking their health status. In recent years, wireless body sensor networks (WBSNs) has an important contribution in Healthcare. In these applications, energy-efficient and secure routing is really critical because health data of individuals must be forwarded to the destination securely to avoid unauthorized access by malicious nodes. However, biosensors have limited resources, especially energy. Recently, energy-efficient solutions have been proposed. Nevertheless, designing lightweight security mechanisms has not been stated in many schemes. In this paper, we propose a secure routing approach based on the league championship algorithm (LCA) for wireless body sensor networks in healthcare. The purpose of this scheme is to create a tradeoff between energy consumption and security. Our approach involves two important algorithms: routing process and communication security. In the first algorithm, each cluster head node (CH) applies the league championship algorithm to choose the most suitable next-hop CH. The proposed fitness function includes parameters like distance from CHs to the sink node, remaining energy, and link quality. In the second algorithm, we employs a symmetric encryption strategy to build secure connection links within a cluster. Also, we utilize an asymmetric cryptography scheme for forming secure inter-cluster connections. Network simulator version 2 (NS2) is used to implement the proposed approach. The simulation results show that our method is efficient in terms of consumed energy and delay. In addition, our scheme has good throughput, high packet delivery rate, and low packet loss rate.

Diffusion and incidence of helium on tungsten surface

In this study, we investigated the diffusion and incidence of helium on tungsten surfaces using first principles and molecular dynamics methods. For three common tungsten surfaces (100, 111, 110), we found that the diffusion barrier of helium is positively/negatively correlated with the interatomic spacing/surface atomic density. The diffusion barriers of helium are relatively similar on the three surfaces, ranging from 5.82 to 6.50 eV. The diffusion barrier of helium on the tungsten surface represents the lower limit of the incidence barrier for helium implantation into tungsten. In our simulation, only helium with an incidence energy greater than 7 eV has the potential to be implanted into tungsten. Generally speaking, the implantation ratio of helium increases with increasing incidence energy and decreases with increasing substrate temperature (although the effect is minor). At a constant temperature, the implantation ratio of helium exhibits an exponential relationship with the incidence energy. The depth distribution of helium can be well described by Gamma distribution function, and the shape and scale parameters of the gamma distribution function can be determined based on the mean and variance of the helium depth distribution. The proposed fitting function allows for the quantitative description of the mean and variance of the helium depth distribution based on the incidence conditions (incident energy E and substrate temperature T), thereby determining the helium depth distribution. This has important implications for the in-depth study of helium-induced tungsten surface fuzz formation.

Efficient energy valley optimization approach for reconfiguring thermoelectric generator system under non-uniform heat distribution

A thermoelectric generator (TEG) suffers from low conversion efficiency, especially when it has been operated under non-uniform temperature distribution. Dynamic reconfiguration is an effective method to enhance the TEG overall efficiency although it requires many electrical switches that make the process more complex. This paper proposes a new approach of energy valley optimizer (EVO) to optimally reconfigure the TEG array operated at non-uniform temperature distribution and enhance the harvested electrical energy. The selection of EVO is due to fast convergence rate, parameter-free, and avoiding stuck in local optima. Minimization of absolute error between the TEG system current at maximum power and the actual one is the proposed fitness function. The analysis is performed on small 9 × 9 TEG array and large 15 × 15 TEG array. Eight temperature distribution patterns of non-uniform row, non-uniform column, short wide, long wide, diagonal, external, internal, and random are analyzed. The proposed EVO is assessed via comparing to reported immune genetic algorithm (IIGA) and other programmed approaches of osprey optimization algorithm (OOA), chef-based optimization algorithm (CBOA), artificial hummingbird algorithm (AHA), and northern goshawk optimizer (NGO). Moreover, statistical investigation is implemented via performing Kruskal-Wallis test, Friedman test, ANOVA test, and Wilcoxon rank test. The fetched results revealed that, the best enhanced power from small 9 × 9 array is 9.143% compared to the original arrangement occurred at long wide temperature distribution. Regarding large 15 × 15 TEG array, the best enhanced power happened during diagonal pattern with 3.359%. The results confirmed the preference of the proposed EVO in solving the TEG system reconfiguration problem with great efficiency.

A novel cluster head selection algorithm based IoT enabled heterogeneous WSNs distributed architecture for smart city

The Internet of Things (IoT) is a revolutionary development that facilitates the instant communication of data among devices, making it an essential paradigm in today's world. It enables the development of smart infrastructure and provides assistance in emergency services during disasters. However, there is a growing need to develop energy-conserving paradigms that cover all aspects of a smart city. In this work, the authors present a hybrid genetic algorithm (GA) inspired greedy strategy-based mutation operation for IoT-enabled heterogeneous wireless sensor networks (WSNs) distributed architecture in a technology driven city. The proposed protocol aims to design a weighted fitness function that considers four parameters: node density, residual and average energy, and distance parameters. Additionally, a 3-tier heterogeneity is considered to prolong the network functional duration, and a deployment strategy is addressed to position these nodes in an energy-efficient manner. The proposed algorithm achieves 31.41%, 20.42%, 13.37%, 1.59% increase in network lifespan compared to GA-based optimized clustering (GAOC), optimized GA with single sink (OptiGAS-StSS), multiple sinks (MS) based GAOC and optimized GA with multiple sink (OptiGAS-StMS), respectively. The significant enhancement in network lifespan is an outcome of choosing the most efficient CH nodes based on the proposed fitness function. Overall, the proposed protocol contributes to developing an energy-conserving paradigm for a smart city.

A new approach for coordinating generated agents’ plans dynamically

In this work, we propose a new approach for coordinating generated agents’ plans dynamically. The purpose is to take into consideration new conflicts introduced in new versions of agents’ plans. The approach consists in finding the best combination which contains one plan for each agent among its set of possible plans whose execution does not entail any conflict. This combination of plans is reconstructed dynamically, each time agents decide to change their plans to take into account unpredictable changes in the environment. This not only ensures that new conflicts are likely to be introduced in the new plans that are taken into account but also it allows agents to deal, solely, with the execution of their actions and not with the resolution of conflicts. For this, we use genetic algorithms where the proposed fitness function is defined based on the number of conflicts that agents can experience in each combination of plans. As part of our work, we used a concrete case to illustrate and show the usefulness of our approach.

Optimum regulation of THD profile in multilevel inverter using parameter-less AI technique for electrical vehicle application

This article proposes investigation on harmonic profile improvement using a novel fitness function in the multiphase multilevel inverter. For the proposed study in this paper, three-phase, seven-level cascaded H-bridge (CHB) multilevel inverter (MLI) is considered. Modulation of the stepped waveform output of the MLI is done using selective harmonic elimination (SHE) method. Many algorithms are proposed for solving the set of nonlinear transcendental trigonometric equations for SHE methods. Teaching learning-based optimisation (TLBO) algorithm is a parameter less optimisation technique. Due to the lack of controlling parameters, the proposed algorithm is most robust among the family of artificial intelligence (AI) techniques. In this paper, an investigation is carried out on a novel fitness function proposed for controlling total harmonic distortion (THD) for the proposed inverter. It is observed that the proposed fitness function improves THD profile below IEEE standards. It is also observed that THD profile obtained through this method is far better than THD profile obtained through various proposed methods so far. The results of THD profile from the MATLAB Simulink simulations are verified experimentally using seven-level three-phase hardware controlled by Arduino MEGA 2560 low-cost controller.

A Spark-Based Artificial Bee Colony Algorithm for Unbalanced Large Data Classification

With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and F1-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size.

Optimizing the Energy Consumption of Blockchain-Based Systems Using Evolutionary Algorithms: A New Problem Formulation

Blockchain technology has gained recognition in industrial, financial, and various technological domains for its potential in decentralizing trust in peer-to-peer systems. A core component of blockchain technology is a consensus algorithm, most commonly Proof of Work (PoW). PoW is used in blockchain-based systems to establish trust among peers; however, it does require the expenditure of an enormous amount of energy that affects the environmental sustainability of blockchain-based systems. Energy minimization, whilst ensuring trust within blockchain-based systems that use PoW, is a challenging problem. The solution has to consider how energy consumption can be minimized without compromising trust, whilst still ensuring, for instance, scalability, security, and decentralization. In this paper, we represent the problem as a subset selection problem of miners in a blockchain-based system. We formulate the problem of blockchain energy consumption as a Search-Based Software Engineering problem with four objectives: energy consumption, carbon emission, decentralization, and trust. We propose a model composed of multiple fitness functions. The model can be used to explore the complex search space by selecting a subset of miners that minimizes the energy consumption without drastically impacting the primary goals of the blockchain technology (i.e., security/trustworthiness and decentralization). We integrate our proposed fitness functions into five evolutionary algorithms to solve the problem of blockchain miners selection. Our results show that the environmental sustainability of blockchain-based systems (e.g. reduced energy use) can be enhanced with little degradation in other competing objectives. We also report on the performance of the algorithms used.

Efficient operation of battery energy storage systems, electric-vehicle charging stations and renewable energy sources linked to distribution systems

In this paper, distribution systems are optimized to accommodate different renewable energy sources, including PhotoVoltaic (PV) and Wind Turbine (WT) units with existing Electric Vehicles Charging stations (EVCS) connected to specific locations of distribution systems. Battery Energy Storage systems (BES) are provided at the exact locations of the PV and WT units. The BES units are optimized to control the power flow inside the distribution systems and enhance their performance by minimizing the proposed fitness functions. Each BES's generated or consumed power depends mainly on its State of Charge (SoC) and system constraints. The Gorilla Troop Optimizer (GTO) algorithm is adopted to solve the optimization problem either as a single-objective or multi-objective optimization. The adopted fitness functions minimize power loss and total voltage deviation (TVD) of the 108-bus distribution system. Two stages are involved in the GTO solution of the optimization problem. The GTO algorithm finds the ideal positions and sizes of the PV and WT units in the first stage. Then the GTO optimizes the BES operation in the second stage after integrating the EVCS into the system, using distinct profiles with two charging levels on weekdays and weekends. The obtained results show that the percentage reductions in the active energy loss with optimal allocations of the PV/WT are 45.29 % and 45.56 % for Level 1 and Level 2 of EVCS charging. The counterpart reductions increase to 48.22 % and 48.33 % when integrating the BES units. Furthermore, with PV/WT integration, utility power consumptions are reduced by 65.74 % and 65.65 % for the two charging levels, while the reductions increase to 70.63 % and 70.54 % when including BES units. These findings emphasize the importance of optimal BES operation in distribution networks equipped with renewable energy sources. The findings demonstrate the superior capabilities of the GTO algorithm during the optimization phase on an equal footing.

Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Context:Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap:Existing works did not take into account the issue of uncertain class labels, which is an important inherent characteristic of the smells detection problem. More precisely, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Unluckily, existing approaches usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. Throwing away and/or disregarding the uncertainty factor could considerably degrade the detection/identification process effectiveness. From a solution approach viewpoint, there is no work in the literature that proposed a method that is able to detect and/or identify code smells while preserving the uncertainty aspect. Objective:The main goal of our research work is to handle the uncertainty factor, issued from human experts, in detecting and/or identifying code smells by proposing an evolutionary approach that is able to deal with anti-patterns classification with uncertain labels. Method:We suggest Bi-ADIPOK, as an effective search-based tool that is capable to tackle the previously mentioned challenge for both detection and identification cases. The proposed method corresponds to an EA (Evolutionary Algorithm) that optimizes a set of detectors encoded as PK-NNs (Possibilistic K-nearest neighbors) based on a bi-level hierarchy, in which the upper level role consists on finding the optimal PK-NNs parameters, while the lower level one is to generate the PK-NNs. A newly fitness function has been proposed fitness function PomAURPC-OVA_dist (Possibilistic modified Area Under Recall Precision Curve One-Versus-All_distance, abbreviated PAURPC_d in this paper). Bi-ADIPOK is able to deal with label uncertainty using some concepts stemming from the Possibility Theory. Furthermore, the PomAURPC-OVA_dist is capable to process the uncertainty issue even with imbalanced data. We notice that Bi-ADIPOK is first built and then validated using a possibilistic base of smell examples that simulates and mimics the subjectivity of software engineers opinions. Results:The statistical analysis of the obtained results on a set of comparative experiments with respect to four relevant state-of-the-art methods shows the merits of our proposal. The obtained detection results demonstrate that, for the uncertain environment, the PomAURPC-OVA_dist of Bi-ADIPOK ranges between 0.902 and 0.932 and its IAC lies between 0.9108 and 0.9407, while for the certain environment, the PomAURPC-OVA_dist lies between 0.928 and 0.955 and the IAC ranges between 0.9477 and 0.9622. Similarly, the identification results, for the uncertain environment, indicate that the PomAURPC-OVA_dist of Bi-ADIPOK varies between 0.8576 and 0.9273 and its IAC is between 0.8693 and 0.9318. For the certain environment, the PomAURPC-OVA_dist lies between 0.8613 and 0.9351 and the IAC values are between 0.8672 and 0.9476. With uncertain data, Bi-ADIPOK can find 35% more code smells than the second best approach (i.e., BLOP). Furthermore, Bi-ADIPOK has succeeded to reduce the number of false alarms (i.e., misclassified smelly instances) by 12%. In addition, our proposed approach can identify 43% more smell types than BLOP and reduces the number of false alarms by 32%. The same results have been obtained for the certain environment, demonstrating Bi-ADIPOK’s ability to deal with such environment.

Intelligent IDS: Venus Fly-Trap Optimization with Honeypot Approach for Intrusion Detection and Prevention

Intrusion Detection Systems and Intrusion Prevention Systems are used to detect and prevent attacks/malware from entering the network/system. Honeypot is a type of Intrusion Detection System which is used to find the intruder, study the intruder and prevent the intruder to access the original system. It is necessary to build a strong honeypot because if it is compromised, the original system can be easily targeted by the attacker. To overcome such challenges an efficient honeypot is needed that can shut the attacker after extracting his attack technique and tools. In this paper, a Venus fly-trap optimization algorithm has been used for implementing the honeypot system along with Intrusion Detection System. Venus plants are a type of carnivorous plants that catch their prey intelligently. By adopting this feature we make an effective honeypot system that will intelligently interact with the attacker. A new fitness function has been proposed to identify size of the attacker. The effectiveness of the proposed fitness function has been evaluated by comparing it with state of the art. For comparison, remote-to-local attacks, probing attacks and DOS attacks are performed on both proposed and existing models. The proposed model is significant to catch/block all the intruders which were caught by the art and also the proposed model reduces the time of interaction between the attacker and honeypot system thereby giving minimum information to the attacker.

A new fitness function in genetic programming for classification of imbalanced data

ABSTRACT Many real-world problems have an uneven distribution of data over different classes. The imbalanced nature of data impacts the performance of classifiers. The higher counts of majority class samples influence the learning abilities of well-known classifiers. Genetic programming (GP) algorithm based on natural evolution also impacts if the data’s nature is imbalanced. The fitness function plays a pivotal role and impacts almost each building block of the GP framework. GP with the standard fitness function produces under-fitted and biased classifiers. Therefore, this paper has proposed a new fitness function in GP to classify the imbalanced data. The proposed method is used to classify nine imbalanced problems: ABL-18, ABL-9-18, BAL, YEAST2, YEAST1, ABL-9, ION, WDBC, and SPECT. The imbalanced factor of benchmark problems varies from 99:1 to 59:41. The proposed method’s performance is compared with K-Nearest-Neighbourhood (KNN) and the standard fitness function-based GP methods. The GP with newly proposed fitness function gives average AUC values for ABL-18(99:1), ABL-9-18(94:6), BAL(92:8), YEAST2(89:11), YEAST1(84:16), ABL-9(83:17), ION(64:36), WDBC(63:37), and SPECT(59:41) as 0.714, 0.812, 0.975, 0.916, 0.768, 0.654, 0.872, 0.939, and 0.704, respectively, which are higher than KNN and the standard fitness function-based GP methods. The result outcomes prove the superiority of the proposed method.

The Detection of Self-Similar/Branching Processes in Complex Biological Systems: Analysis of the Temporal Evolution of Impedance Measurements in Tulsi (Holy Basil) Leaves "Ocimumtenuiflorum".

In this paper, a theoretical model has been proposed for the first time to illustrate the living Tulsi (Holy Basil) leaf evolution in describing the branching systems in the intermediate range of frequencies. The proposed fitting function following the applied model portrays the whole stages of the temporal evolution of Tulsi (Holy Basil, "Ocimumtenuiflorum") leaf during the six days of impedance measurements with a high degree of accuracy (with fitting error less than 0.1 percent). The developed fitting parameter enables to identify the three stages of temporal evaluation of the Tulsi leaf as "vital leaf activity" (first stage), "quasi-chaotic behaviour" (second stage), and the "dying" (third stage). The theoretical model proposed in this work incorporated a fractal element having complex conjugated power law of exponents and a fractal element with a time lagged branching process. This novel approach introduces an additional degree of freedom over the previously proposed impedance models in terms of its imaginary part of complex conjugated power-law exponent. Further, it increases flexibility and versatility in accurately modeling the behavioral variations of complex branching systems, whose seemingly nondeterministic temporal nature had been considered earlier as counterintuitive and random. The incorporation of branching processes in comprehensively explaining the complex biological systems allows us to gain a deeper insight into the transfer of charge processes in the intermediate range of available frequency scales. Furthermore, the proposed model validates the presence of new fractal elements with a complex conjugated power law of exponents in naturally occurring biological processes. The experimental confirmation can play a key role in explaining a wide class of branching processes in complex systems and enrich the modern theory of fractional calculus.

LCIPA: Lightweight clustering protocol for industry 4.0 enabled precision agriculture

Smart agriculture using the Internet of Things (IoT) is an Industry 4.0 technology used in rural and urban environments. Industry 4.0 is a new class of the Industrial Revolution that focuses heavily on automation, interconnection, real-time data, and artificial intelligence (AI). With the growing demand for agricultural automation to increase farm output while meeting minimal criteria, smart farming has emerged as one of the fundamental applications of Industry 4.0. We propose an AI-based smart farming protocol since AI approaches are essential for the performance improvement of Industry 4.0 standards. We design Lightweight Clustering Protocol for Industry 4.0 Enabled Precision Agriculture (LCIPA) protocol via clustering and routing algorithms. Smart farming such as Industry 4.0, tackles long-distance communications, energy efficiency, computing efficiency, and assured QoS performance. In the clustering phase, we compute the direct and indirect sensor node parameters to produce the integrated fitness function value. The optimal selection of Cluster Head (CH) and reliable data forwarders is obtained using this novel fitness function. We establish the objective function for clustering and routing to reduce energy consumption and communication overhead while maximizing network performance using an integrated fitness function. The proposed fitness function is used to develop the Bacterial Foraging Algorithm (FBA) for optimum CH selection. A lightweight route discovery approach is based on periodic fitness ratings for inter-cluster and intra-cluster data transfer without specific processing. The simulation results reveal the efficiency of the LCIPA protocol compared to underlying methods.

BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets.

The relationship between the host and the microbiome, or the assemblage of microorganisms (including bacteria, archaea, fungi, and viruses), has been proven crucial for its health and disease development. The high dimensionality of microbiome datasets has often been addressed as a major difficulty for data analysis, such as the use of machine-learning (ML) and deep-learning (DL) models. Here, we present BiGAMi, a bi-objective genetic algorithm fitness function for feature selection in microbial datasets to train high-performing phenotype classifiers. The proposed fitness function allowed us to build classifiers that outperformed the baseline performance estimated by the original studies by using as few as 0.04% to 2.32% features of the original dataset. In 35 out of 42 performance comparisons between BiGAMi and other feature selection methods evaluated here (sequential forward selection, SelectKBest, and GARS), BiGAMi achieved its results by selecting 6–93% fewer features. This study showed that the application of a bi-objective GA fitness function against microbiome datasets succeeded in selecting small subsets of bacteria whose contribution to understood diseases and the host state was already experimentally proven. Applying this feature selection approach to novel diseases is expected to quickly reveal the microbes most relevant to a specific condition.

A Novel Binary Hybrid PSO-EO Algorithm for Cryptanalysis of Internal State of RC4 Cipher

Cryptography protects privacy and confidentiality. So, it is necessary to guarantee that the ciphers used are secure and cryptanalysis-resistant. In this paper, a new state recovery attack against the RC4 stream cipher is revealed. A plaintext attack is used in which the attacker has both the plaintext and the ciphertext, so they can calculate the keystream and reveal the cipher’s internal state. To increase the quality of answers to practical and recent real-world global optimization difficulties, researchers are increasingly combining two or more variations. PSO and EO are combined in a hybrid PSOEO in an uncertain environment. We may also convert this method to its binary form to cryptanalyze the internal state of the RC4 cipher. When solving the cryptanalysis issue with HBPSOEO, we discover that it is more accurate and quicker than utilizing both PSO and EO independently. Experiments reveal that our proposed fitness function, in combination with HBPSOEO, requires checking 104 possible internal states; however, brute force attacks require checking 2128 states.

Parallel Meta-Heuristics for Solving Dynamic Offloading in Fog Computing

The internet of things (IoT) concept has been extremely investigated in many modern smart applications, which enable a set of sensors to either process the collected data locally or send them to the cloud for remote processing. Unfortunately, cloud datacenters are located far away from IoT devices, and consequently, the transmission of IoT data may be delayed. In this paper, we investigate fog computing, which is a new paradigm that overcomes many issues of cloud computing. More importantly, dynamic task offloading in fog computing is a challenging problem that requires an optimal decision for processing the tasks that are generated in each time slot. Thus, exact optimization methods based on Lyapunov function have been widely used for solving dynamic offloading which represents an NP hard problem. To overcome the scalability issue of exact optimization techniques, we have explored famous population based meta-heuristics for optimizing the offloading process of a set of dynamic tasks using Orthogonal Frequency Division Multiplexing (OFDM) communication. Hence, a parallel multi-threading framework is proposed for generating the optimal offloading solution while selecting the best sub-carrier for each offloaded task. More importantly, our contribution associates a thread for each IoT device and generates a population of random solutions. Next, each population is updated and evaluated according to the proposed fitness function that considers a tradeoff between the delay and energy consumption. Upon the arrival of new tasks at each time slot, an evaluation is performed for maintaining some individuals of the previous population while generating new individuals based on some criteria. Our results have been compared to the results achieved using Lyapunov optimization. They demonstrate the convergence of the fitness function, the scalability of the parallel Particle Swarm Optimization (PSO) approach, and the performance in terms of the offline error and the execution cost.

Empirical analyses of genetic algorithm and grey wolf optimiser to improve their efficiency with a new multi-objective weighted fitness function for feature selection in machine learning classification: the roadmap

ABSTRACT Feature selection (FS) is an optimisation problem that reduces the dimension of the dataset and increases the performance of the machine learning algorithms and classification through the selection of the optimal subset features and elimination of the redundant features. However, the huge search space is an important challenge in the FS problem. Due to their satisfactory capabilities to handle high-dimension search spaces, meta-heuristic search algorithms have recently gained much attention and become popular in the FS problem. For these algorithms, choosing a proper fitness function plays an important role. The fitness function orients the searching strategy of the algorithms to obtain best solutions. Appropriate fitness functions will help the algorithms with exploring the search space more effectively and efficiently. In this work, firstly the efficiency of two of the most outstanding and successful heuristic algorithms in the FS domain, namely genetic algorithm (GA) and grey wolf optimiser (GWO), are investigated and analysed with a single-objective fitness function. Secondly, two recent feature selection techniques based on GA and GWO, namely feature selection, weight, and parameter optimisation (FWP) and binary GWO (BGWO) with their fitness function are investigated and analysed. Thirdly, in order to remove the detected drawbacks and weaknesses of the FS algorithms and to enhance their efficiency, a new multi-objective weighted fitness function based on multiple predominant criteria has been presented. The effectiveness of the proposed fitness function on the FS algorithms is evaluated by using SVM and associative classification on 11 different large and small datasets. The experimental results show the superiority of proposed fitness function (where features were reduced and the classification performance has been improved) over single-objective fitness function and other existing fitness functions. Furthermore, another key aim of this study is to present a comprehensive study about the strengths and weaknesses of the FS algorithms which can be used as guidelines for future possible works to more improve the developments of these algorithms.

Metaheuristic Load-Balancing-Based Clustering Technique in Wireless Sensor Networks

The resource-constrained nature of wireless sensor networks engenders the development of energy-efficient network operations. To mitigate the prime concern of developing an energy-efficient network, clustering of the nodes has emerged as a very effective tool. If executed intelligently, clustering can not only help in obtaining even load distribution among the network nodes but also help in having the enhanced network lifetime and scalability. In this work, a Metaheuristic Load-Balancing-Based Clustering Technique (MLBCT) in wireless sensor networks has been proposed which formulates the energy-balanced clusters based on the differential evolution technique to improve the network lifetime. To ensure the formation of balanced clusters, several metrics like nodes’ proximity, nodes’ distribution, and energy distribution across the sensing field have been considered. Moreover, to facilitate the even load distribution among the cluster members, a randomized rotation of cluster head is implemented. The supremacy of the proposed scheme is confirmed through an extensive set of simulations against the state-of-art schemes. Simulation results reflect an average gain of 51.85% in network lifetime under the variable network configurations in an ideal environment. Moreover, a thorough statistical analysis is performed to prove the efficacy of the proposed fitness function by obtaining confidence intervals under two different network scenarios with variable node counts.

A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization.

The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) is now critical for the provision of efficient resource management in cloud data centers. The VMP process considers the placement of a set of virtual machines onto a set of physical machines, in accordance with a set of criteria. The optimal solution for multi-objective VMP can be determined by using a fitness function that combines the objectives. This paper proposes a novel model to enhance the performance of the VMP decision-making process. Placement decisions are made based on a fitness function that combines three criteria: placement time, power consumption, and resource wastage. The proposed model aims to satisfy minimum values for the three objectives for placement onto all available physical machines. To optimize the VMP solution, the proposed fitness function was implemented using three optimization algorithms: particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization (FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was tested experimentally. The results of the comparative study between the three algorithms show that the hybrid algorithm has the strongest performance. Moreover, the proposed algorithm was tested against the bin packing best fit strategy. The results show that the proposed algorithm outperforms the best fit strategy in total server utilization.