Efficient Metaheuristic Algorithm Research Articles

SAIoT: Scalable Anomaly-Aware Services Composition in CloudIoT Environments

Among the novel IT paradigms, cloud computing and the Internet of Things (CloudIoT) are two complementary areas designed to support the creation of smart cities and application services. The CloudIoT not only presents ubiquitous services through IoT nodes but it also provides virtually unlimited resources through services composition. The services composition problem aims to find a set of services among functionally equivalent services with different Quality of Service (QoS) concerning users’ constraints. To this aim, previous studies calculate QoS values through service logs without considering the presence of anomalies in the existing QoS values; however, the dynamicity of distributed service environments and communication networks in CloudIoT environments causes anomalies in the QoS values. Therefore, existing approaches fail to model QoS values accurately that leads to service-level agreement (SLA) violation and penalties for service broker. To address this challenge, we propose a scalable anomaly-aware approach (SAIoT) including two main components: the first component models QoS values based on a machine learning anomaly detection technique, to remove the existing abnormal QoS records, and the second component finds a near-optimal composition by using an effective and efficient metaheuristic algorithm. The experimental results based on real-world data sets show that our approach achieves 30.64% of the average improvement in the QoS value of a composite plan with equal or even less price compared to the previous works, such as information theory-based and advertised QoS-based methods.

Image segmentation with Kapur, Otsu and minimum cross entropy based multilevel thresholding aided with cuckoo search algorithm

Color image segmentation is the primary factor to provide the intended information from the input image. The straightforward method called multilevel thresholding (MLT) is used to analyse the various classes of complex images. But, when the level of threshold increases, computational difficulty increases. Hence, MLT with most promising objective functions such as Kapur, Otsu and minimum cross entropy aided with cuckoo search algorithm (CSA) is used. The efficient metaheuristic cuckoo search algorithm’s controlling parameter balances the local and global search. In this paper, the efficacy of CSA at 4,5,6 and 7 threshold levels with various fitness functions are utilized for precise image segmentation. It is seen from experimental results, the Otsu based cuckoo search algorithm outperform than Kapur and MCE. Quality metrices such as computational time, PSNR (peak signal to noise ratio) and SSIM (structural similarity index) authenticate the exploration and exploitation capability of CSA algorithm for real-world applications.

Image-based Visual Servoing Control of a Quadcopter Air Vehicle

ABSTRACT This article proposes two optimal controllers that stabilize the altitude, attitude, heading, and position of the quadcopter in space. The first optimal control method consists of computing the proportional controller gain by minimizing the error between measured and the desired feature vector values, as opposed to the classical visual servoing (VS) scheme that requires a known value of the proportional controller gain which is often chosen empirically. The second control method relies on the optimal tuning of PD controller gains applied on the altitude, attitude, and position loops. This, however, may lead to a convergence problem, which is overcome by the use of an optimization technique, the bat algorithm (BA). This latter is an efficient metaheuristic algorithm that has been successfully used in many optimization problems. Several simulations are run in MATLAB, in which visual servo control of the Vertical Takeof and Landing (VTOL) type of Unmanned Aerial Vehicles (UAVs), known as the quadcopter, is implemented. The obtained results show that the Bat algorithm renders the classical PD controller adaptive and provides a high flexibility in tracking the UAV trajectory even in the presence of disturbances. It also guarantees the stability and accuracy of the optimal, minimal time, vision-based controller.

An innovative hybrid algorithm for bound-unconstrained optimization problems and applications

Particle swarm optimization (PSO) and differential evolution (DE) are two efficient meta-heuristic algorithms, achieving excellent performance in a wide variety of optimization problems. Unfortunately, when both algorithms are used to solve complex problems then they inevitably suffer from stagnation, premature convergence and unbalanced exploration–exploitation. Hybridization of PSO and DE may provide a platform to resolve these issues. Therefore, this paper proposes an innovative hybrid algorithm (ihPSODE) which would be more effective than PSO and DE. It integrated with suggested novel PSO (nPSO) and DE (nDE). Where in nPSO a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of ihPSODE population best half member has been identified and discard rest members. Further, in current population nPSO is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed ihPSODE and its integrating component nPSO and nDE have been tested over 23 basic, 30 IEEE CEC2014 and 30 IEEE CEC2017 unconstrained benchmark functions plus 3 real life optimization problems. The performance of proposed algorithms compared with traditional PSO and DE, their existed variants/hybrids as well as some of the other state-of-the-art algorithms. The results indicate the superiority of proposed algorithms. Finally, based on overall performance ihPSODE is recommended for bound-unconstrained optimization problems in this present study.

Crystal Structure Algorithm (CryStAl): A Metaheuristic Optimization Method

Metaheuristics are computational procedures that intelligently lead the search process through the efficient exploration of the search space associated with an optimization problem. With the progressive outburst of problems with large data sets in various fields, there is an ongoing quest for enhancing existing metaheuristic algorithms as well as developing new ones with greater accuracy and efficiency. In general, a powerful and efficient metaheuristic algorithm is based on a rich inspiration source, implemented effectively through a precise mathematical model. Aiming to develop a highly efficient, nature-inspired optimization algorithm, here we propose a novel metaheuristic called Crystal Structure Algorithm (CryStAl). This method is chiefly inspired by the principles underlying the formation of crystal structures from the addition of the basis to the lattice points, which is a natural phenomenon that can be seen in the symmetric arrangement of constituents (i.e. atoms, molecules, or ions) in crystalline minerals such as quartz. A total number of 239 mathematical functions which are categorized into four different groups are utilized to evaluate the overall performance of the proposed method. To validate the results of this novel algorithm, 12 different classical and modern metaheuristic algorithms are selected from the literature. The minimum, mean, and standard deviation values alongside the number of function evaluations for CryStAl and the other metaheuristics for a specific tolerance are calculated and presented accordingly. The obtained results, further supported by a complete statistical analysis, demonstrated that the proposed algorithm is capable of providing very competitive results, outperforming the other metaheuristics in most cases.

An efficient genetic algorithm for solving open multiple travelling salesman problem with load balancing constraint

The multiple travelling salesman problem (MTSP) is one of the widely studied combinatorial optimization problems with various theoretical and practical applications. However, most of the studies intended to deal with classical MTSP, very limited attention has been given to an open multiple travelling salesman problem and its variants. In this paper, an open multiple travelling salesman problem with load balancing constraint (OMTSPLB) is addressed. The OMTSPLB differs from the conventional MTSP, in which all the salesmen start from the central depot and need not come back to it after visiting the given number of cities by accomplishing the load balance constraint, which helps in fairly distributing the task among all salesmen. The problem aims to minimize the overall traversal distance/cost for operating open tours subject to the load balancing constraint. A zero-one integer linear programming (0-1 ILP) model and an efficient metaheuristic genetic algorithm (GA), is established for the OMTSPLB. Since no existing study on OMTSPLB, the proposed GA is tested on the relaxed version of the present model, comparative results are reported. The comparative results show that the proposed GA is competent over the existing algorithms. Furthermore, extensive experiments are carried out on OMTSPLB and the results show that proposed GA can find the global solution effectively.

A biobjective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer

AbstractHome health care (HHC) logistics have become a hot research topic in recent years due to the importance of HHC services for the care of ageing population. The logistics of HHC services as a routing and scheduling problem can be defined as the HHC problem (HHCP) academically including a set of service centers and a large number of patients distributed in a specific geographic environment to provide various HHC services. The main challenge is to provide a valid plan for the caregivers, who include nurses, therapists, and doctors, with regard to different difficulties, such as the time windows of availability for patients, scheduling of the caregivers, working time balancing, the time and cost of the services, routing of the caregivers, and route balancing for their routes. This study establishes a biobjective optimization model that minimizes (i) the total service time and (ii) the total costs of HHC services to meet the aforementioned limitations for the first time. To the best of the authors’ knowledge, this research is the first of its kind to optimize the time and cost of HHC services by considering the route balancing. Since the model of the developed HHCP is complex and classified as NP-hard, efficient metaheuristic algorithms are applied to solve the problem. Another innovation is the development of a new self-adaptive metaheuristic as an improvement to the social engineering optimizer (SEO), so-called ISEO. An extensive analysis is done to show the high performance of ISEO in comparison with itself and two well-known metaheuristics, i.e. FireFly algorithm and Artificial Bee Colony algorithm. Finally, the results confirm the applicability of new suppositions of the model and further development and investigation of the ISEO more broadly.

A Review of Metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks

The optimal integration of electrical units, such as distributed generation units, power electronic devices, and electric vehicles, is a significant development of smart grids. This development has effectively transformed the traditional grid system, promising numerous advantages for economic values and autonomous energy source control. In smart grids development, metaheuristic algorithms are one of the optimization algorithms that have been applied extensively to mitigate the accompanying problems such as voltage instability, power loss, and high installation cost. This paper presents a comprehensive review of metaheuristic techniques for the optimal integration of electrical units in distribution networks, considering different phases of the optimization process. These include the approaches for handling of crucial objective functions and the optimal integration methods for different electrical units. This review shows a need for more research on developing efficient metaheuristic algorithms and the effective handling of multiple objective functions.

An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment

Detection of Denial of Service (DoS) attack is one of the most critical issues in cloud computing. The attack detection framework is very complex due to the nonlinear thought of interruption activities, unusual conduct of systems traffic, and many attributes in the issue space. This paper proposes an efficient DoS attack detection system that uses the Oppositional Crow Search Algorithm (OCSA), which integrates the Crow Search Algorithm (CSA) and Opposition Based Learning (OBL) method to address such type of issues. The proposed system consists of two stages viz. selection of features using OCSA and classification using Recurrent Neural Network (RNN) classifier. The essential features are selected using the OCSA algorithm and then given to RNN classifier. In the subsequent testing process, incoming data is classified using the RNN classifier. It ensures the separation of standard data (saved in cloud) and the removal of compromised data Using the benchmark data set, the results of experimental evaluation demonstrate that the proposed technique outperforms the other conventional methods by 98.18%, 95.13%, 93.56%, and 94.12% in terms of Precision, Recall, F-Measure, and Accuracy respectively. Further, the proposed work outperforms existing works by 3% on an average for all the metrics used.

A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home

Abstract In this paper, the min-conflict local search algorithm (MCA) is hybridized with the grey wolf optimizer (GWO) for the power scheduling problem in smart home (PSPSH), and the proposed method is called GWO-MCA. MCA is utilized as a new operator of GWO to improve its exploitation capability in addressing constraint satisfaction problems, particularly scheduling problems. GWO is one of the most efficient metaheuristic algorithms which mimics the hunting behavior of grey wolves. PSPSH is a problem of scheduling smart home appliances in accordance with a dynamic pricing scheme(s) to flatten users’ power consumption. PSPSH’s objectives are to reduce electricity bills, improve user comfort, and maintain power systems’ performance. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. GWO-MCA is tested by using up to 36 appliance operations for 7 days. To show the effect of MCA on the convergence behavior of optimization problems, MCA is utilized as a new operator on other five popular optimization methods: genetic algorithm, particle swarm optimization, wind-driven optimization, antlion optimizer, and enhanced differential evolution. Interestingly, MCA shows a high impact on the performance of such algorithms. In addition, GWO-MCA achieves a better schedule than all compared MCA-Based methods in tackling PSPSH. In addition, the results of GWO-MCA are compared with those of three state-of-the-art hybrid methods to verify the GWO-MCA performance. The GWO-MCA excels the other comparative methods in almost all datasets used. Besides, the GWO-MCA method is compared with 20 state-of-the-art methods using their datasets. Again, GWO-MCA is able to outperform them for almost all datasets used.

Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050

The empirical success of the Artificial Intelligence (AI), has enhanced importance of the transparency in black box Machine Learning (ML) models. This study pioneers in developing an explainable and interpretable Deep Neural Network (DNN) model for a Guideless Irregular Dew Point Cooler (GIDPC). The game theory based SHapley Additive exPlanations (SHAP) method is used to interpret contribution of the operating conditions on performance parameters. Furthermore, in a response to the endeavours in developing more efficient metaheuristic optimisation algorithms for the energy systems, two Evolutionary Optimisation (EO) algorithms including a novel bio-inspired algorithm i.e., Slime Mould Algorithm (SMA), and Particle Swarm Optimization (PSO), are employed to simultaneously maximise the cooling efficiency and minimise the construction cost of the GIDPC. Additionally, performance of the optimised GIDPCs are compared in both statistical and deterministic way. The comparisons are carried out in diverse climates in 2020 and 2050 in which the hourly future weather data are projected using a high-emission scenario defined by Intergovernmental Panel for Climate Change (IPCC). The results revealed that the hourly COP of the optimised systems outperform the base design. Although power consumption of all systems increases from 2020 to 2050, owing to more operating hours as a result of global warming, but power savings of up to 72%, 69.49%, 63.24%, and 69.21% in hot summer continental, Arid, tropical rainforest and Mediterranean hot summer climates respectively, can be achieved when the systems run optimally.

Adaptive Genetic Algorithm for Hydro-thermal Unit Commitment Considering the Security Constraints

This paper proposes a new approach with two efficient metaheuristic algorithms, in combination with quadratic programming, to solve the nonlinear optimization problem Unit Commitment in a complex hydro-thermal power system i.e. Hydrothermal Unit Commitment (HTUC). The main goal is to minimize the total costs (which are a very non-linear and non-convex problem), while satisfying the many hydro-thermal constraints. Such constraints, together with the nonlinear non-convex and mixed-integer objective function, make the search space extremely complex. To solve such a complicated system, the paper proposes a hybridization of a developed binary-coded genetic algorithm (in which quadratic programming is integrated), with a particle swarm optimization (PSO) algorithm. PSO is applied to the final economic load dispatch (ELD), based on the optimal binary combination obtained from the genetic algorithm. A new approach has been proposed through the application of a repair mechanism, which is based on a priority list, in order to maintain the diversity of the population and prevent premature convergence. The entire algorithm was developed and tested in MATLAB and then applied to the IEEE 30 BUS test system. The experimental results show better performance of the proposed algorithm compared to the recently published algorithms, in terms of convergence, constraint handling, as well as better solution quality.

Localizing and quantifying structural damage by means of a beetle swarm optimization algorithm

An efficient meta-heuristic algorithm, named beetle swarm optimization (BSO), is proposed to localize and quantify structural damage using limited vibration measurement data. The beetle antennae search (BAS) algorithm that imitats a random walking mechanism in nature was recently developed to solve the optimization problem. However, the ratio of convergence of this algorithm significantly relys on the random direction and deviation for high-dimensional problems. To overcome this shortcoming, the BSO inspired by the swarm intelligence strategy is proposed. In the iterative search process of the BSO, each beetle swarm moves in a random direction like the BAS and the swarm of beetles is cognitive with the optimal one for the searching behavior. Consequently, the optimal one is updated step by step until a better beetle appears. To demonstrate the capability and robustness of the BSO, numerical and experimental studies using limited vibration measurement data of an offshore wind turbine structure are carried out for structural damage identification. An novel objective function is established by combining natural frequencies with mode shapes of the structure. The numerical results show that the BSO can accurately localize and quantify various types of damage even in a noise and temperature variations polluted environment. Moreover, it has higher accuracy and faster convergence speed than the BAS and the particle swarm optimization (PSO) algorithms. These promising performances could contribute to establishing a structural monitoring system for safety assurance of wind turbine structures.

A robust bi-objective location-routing model for providing emergency medical services

PurposeThis paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).Design/methodology/approachA shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.FindingsThe results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.Research limitations/implicationsIn this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.Practical implicationsThe outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. The proposed algorithm can obtain acceptable solutions for real-world cases.Originality/valueA novel robust mixed-integer linear programming (MILP) model is proposed to formulate the problem as a LRP. To solve the problem, two efficient metaheuristic algorithms were developed to determine the optimal values of objectives and decision variables.

Hybrid fractional fuzzy PID design for MPPT-pitch control of wind turbine-based bat algorithm

The power quality and protection enhancement of wind turbine becomes an important paramount. This requires a competitive controller with high performances. For this purpose, the paper presents a novel hybrid controller named fractional fuzzy PID (FFPID) strategy tunning with an efficient metaheuristic optimization bat algorithm (BA) for controlling the wind turbine equipped with permanent magnet synchronous generator (WT-PMSG). The main control objectives are to drive WT-PMSG for extracting the maximum power point (MPPT) in the low range of weather conditions and further to protect the wind turbine in high-wind region using pitch angle control. The performance of proposed strategy is analysed for all regions low–high wind speed disturbances. The obtained results are obviously proved that the optimal hybrid fractional-order fuzzy PID controller is highly better than the optimal classical PID, fuzzy PID (FPID) and fractional-order PID (FOPID) controllers in terms of response, overshoot and indices performances. Further, the WT-PMSG is effectively controlled at different conditions of wind velocities by the proposed scheme.

Optimisation of Shear and Lateral–Torsional Buckling of Steel Plate Girders Using Meta-Heuristic Algorithms

The shear buckling of web plates and lateral–torsional buckling are among the major failure modes of plate girders. The importance of the lateral–torsional buckling capacity of plate girders was further evidenced when several plate girders of a bridge in Edmonton, Alberta, Canada failed in 2015, because insufficient bracing led to the lateral buckling of the plate girders. In this study, we focus on the optimisation of the cross-sections of plate girders using a well-known and extremely efficient meta-heuristic optimisation algorithm called the harmony search algorithm. The objective of this optimisation is to design the cross-sections of the plate girders with the minimum area that satisfies requirements, such as the lateral–torsional buckling load and ultimate shear stress. The base geometry, material properties, applied load and boundary conditions were taken from an experimental study and optimised. It was revealed that the same amount of load-carrying capacity demonstrated by this model can be achieved with a cross-sectional area 16% smaller than that of the original specimen. Furthermore, the slenderness of the web plate was found to have a decisive effect on the cost-efficiency of the plate girder design.

Comparative Analysis of Bio-Inspired Algorithms for Underwater Wireless Sensor Networks

Mobile nodes in underwater wireless sensor networks are becoming very important as they not only enable flexible sensing areas but also entails the ability to provide means for data and energy sharing among existing static sensor nodes. In this paper, three efficient meta-heuristic evolutionary algorithms ant colony optimization, artificial bees colony and firefly algorithm, inspired by swarm intelligence are being compared with an objective to achieve the shortest path for the mobile node in traversing the complete sensing network. We transform this problem into the traveling salesman problem. It is the most famous and commonly used nondeterministic-polynomial combinatorial optimization problem in which an artificial agent is set to travel between different cities and calculate distance or time consumed to travel between these nodes or cities for best route selection. Heuristic and meta-heuristic algorithms are being used for decades to solve such type of problems. In this comparative study, an analysis of meta-heuristic algorithms for obtaining results in less processing time while searching for the optimal solution has been done. Moreover, this paper provides a classification of mentioned algorithms and highlights their characteristics. The experiment has been carried out on these algorithms by manipulating different parameters such as population and number of iteration.

Transient search optimization for electrical parameters estimation of photovoltaic module based on datasheet values

This paper presents a novel efficient metaheuristic algorithm called Transient Search Optimization (TSO), which is inspired by the transient process of the inductive and capacitive circuits. Also, this paper presents an objective function based on the datasheet of PV modules at standard test conditions (STC). Then, the TSO algorithm is applied to minimize the objective function to find the optimal nine parameters of the three-diode model (TDM) of the PV module. Also, the results of the proposed TSO algorithm are compared with that obtained by using other metaheuristic algorithms, where in this regard the TSO achieved the best results. The proposed technique is verified by applying it to find the optimal TDM of three commercially common PV modules with different cell types, rated power, and terminal voltage. Then, the simulated I-V and P-V characteristics of these PV modules matched with the measured data under many environmental conditions. Accordingly, the results have proved that the offered technique is useful to find the optimal TDM of all PV modules based on the dataset given by the manufacturers.

A new Newton metaheuristic algorithm for discrete performance-based design optimization of steel moment frames

In this paper a new and efficient metaheuristic algorithm is proposed for discrete performance-based seismic design optimization of steel moment frames. The proposed metaheuristic uses the Newton gradient-based method as its updating scheme in a population-based framework and therefore it is termed as Newton Metaheuristic Algorithm (NMA). In order to enable the NMA to effectively explore the discrete design space, a term containing the best solution found is added to the basic updating rule of the algorithm. In addition, a simple and efficient method is proposed in order to establish a balance between local and global search abilities of the proposed algorithm. The efficiency of the NMA is illustrated by presenting two benchmark discrete truss optimization problems. Moreover, three steel moment frames are optimized in the framework of performance-based design by the NMA and the results are compared with those of some recent metaheuristics. The performance of the algorithms is analyzed using statistical parametric and non-parametric tests indicating that NMA outperforms the other algorithms in literature.

Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things

One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.