Heuristic Optimization Algorithm Research Articles

Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle–based network coverage optimization

The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of unmanned aerial vehicles in the future, we propose a new heuristic algorithm for coverage optimization. The proposed algorithm is implemented based on a conditional generative adversarial neural network, with a unique multilayer sum-pooling loss function. To assess the performance of the proposed approach, we compare it with the optimal core-set algorithm and quasi-optimal spiral algorithm. Simulation results show that the proposed approach converges to the quasi-optimal solution with a negligible difference from the global optimum while maintaining a quadratic complexity regardless of the number of users.

Meta Heuristic Optimization Based Renewable Energy Penetration Enhancement in Microgrid with Demand Response

A model with a combination of cogeneration and generation photovoltaic power is designed for enhancing the capability of photoelectric absorption. The initial step of the model includes using a “time-of-use price strategy” for guiding the users for changing their electricity consumption patterns to participate in the demand response, hence establishing the demand response model. Next the MATLAB Environment Meta Heuristic Optimization Algorithms of Genetic Algorithm (GA), Particle Swarm Optimization Algorithm (PSO) and Ant Colony Algorithms (ACO) was applied with an aim of reducing the price of the operation of the micro-grid to achieve economic dispatching. The model includes, the constraints of power balance equation, energy storage unit operation, as well as the heat storage. The last part of the research details the results, depicting enhanced levels of consumption of photo-electric energy with the proposed model with comparison of Three Algorithms and also produces economic benefits from using the microgrid.

Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments

Cloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In this paper, we mainly focus on the efficient scheduling of distributed simulation tasks in collaborative cloud and edge environments. Since simulation tasks are usually tightly coupled with each other by sending many messages and the status of tasks and hosts may also change frequently, it is essentially a dynamic bin-packing problem. Unfortunately, popular methods, such as meta-heuristics, and accurate algorithms are time-consuming and cannot deal with the dynamic changes of tasks and hosts efficiently. In this paper, we present Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task. After formulating such a scheduling problem as a min-cost max-flow (MCMF) problem, incremental MCMF algorithms are adopted to accelerate the procedure of calculating an optimal flow and heuristic scheduling algorithm, with the awareness of task migration cost, designed to assign tasks. Simulation experiments on Alibaba cluster trace show that Pool can schedule all of the tasks efficiently and is almost 5.8 times faster than the baseline method when few tasks and hosts change in the small problem scale.

Heuristic algorithms in evolutionary computation and modular organization of biological macromolecules: Applications to in vitro evolution.

Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches, significantly more efficient than classical selectionist schemes, were found. This is especially true for some families of special problems. There are strong arguments to believe that EC approaches are quite suitable for modeling and numerical analysis of those methods of synthetic biology and biotechnology that are known as in vitro evolution. Therefore, it is natural to expect that the new algorithms and approaches developed in EC can be effectively applied in experiments on the directed evolution of biological macromolecules. According to the John Holland's Schema theorem, the effective evolutionary search in genetic algorithms (GA) is provided by identifying short schemata of high fitness which in the further search recombine into the larger building blocks (BBs) with higher and higher fitness. The multimodularity of functional biological macromolecules and the preservation of already found modules in the evolutionary search have a clear analogy with the BBs in EC. It seems reasonable to try to transfer and introduce the methods of EC, preserving BBs and essentially accelerating the search, into experiments on in vitro evolution. We extend the key instrument of the Holland's theory, the Royal Roads fitness function, to problems of the in vitro evolution (Biological Royal Staircase, BioRS, functions). The specific version of BioRS developed in this publication arises from the realities of experimental evolutionary search for (DNA-) RNA-devices (aptazymes). Our numerical tests showed that for problems with the BioRS functions, simple heuristic algorithms, which turned out to be very effective for preserving BBs in GA, can be very effective in in vitro evolution approaches. We are convinced that such algorithms can be implemented in modern methods of in vitro evolution to achieve significant savings in time and resources and a significant increase in the efficiency of evolutionary search.

Vibration mitigation of pile groups by coupling a wave-based vibration analysis and genetic algorithm

ABSTRACT This paper presents a coupled wave-based/genetic algorithm approach for vibration mitigation in a structure built on a pile group. The wave-based method considers the structure and foundation as a whole waveguide system in which the equations of wave propagation at waveguides and wave refraction at discontinuities are solved analytically using Timoshenko beam theory. These solutions are then assembled and solved numerically within a single system matrix. To model the ground, the viscoelastic soil around the piles is modelled using spring-dashpot elements. The wave-based method is used to evaluate a fitness function within a genetic algorithm procedure, and the resulting coupled tool is used for finding the optimal geometric characteristics of a pile group. The obtained results highlight the efficiency of the wave-based approach to be used in heuristic optimization algorithms such as genetic algorithm. In addition, it is observed that the optimal layout of the pile group depends significantly on loading frequency and soil properties.

Swarm intelligence-based technique to enhance performance of ANN in structural damage detection

Artificial neural network (ANN), a powerful technique, has been used widely over the last decades in many scientific fields including engineering problems. However, the backpropagation algorithm in ANN is based on a gradient descent approach. Therefore, ANN shows high potential in local stagnancy. Besides, choosing the right architecture of ANN for a specific issue is not an easy task to deal with. This paper introduces a simple, effective hybrid approach between an optimization algorithm and a traditional ANN for damage detection. The global search-ability of a heuristic optimization algorithm, namely grey wolf optimizer (GWO), can solve the drawbacks of ANN and also improve the performance of ANN. Firstly, the grey wolf optimizer is used to update the finite element (FE) model of a laboratory steel beam based on the vibration measurement. The updated FE model of the tested beam then is used to generate data for network training. For an effective training process, GWO is utilized to identify the optimal parameters for ANN, such as the number of the hidden nodes, the proportion of dataset for training, validation, test, and the training function. The optimization process provides an optimal structure of ANN that can be used to predict the damages in the beam. The obtained results confirm the accuracy, effectiveness, and reliability of the proposed approach in (1) alleviating the differences between measurement and simulation and (2) damage identification including damage location and severity, in the tested beam considering noise effects. For both applications, dynamic characteristics like natural frequencies and mode shapes of the beam derived from the updated FE model, are collected to calculate the objective function

Flow-Shop Scheduling with Transportation Capacity and Time Consideration

Planning and scheduling is one of the most important activity in supply chain operation management. Over the years, there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains. This includes two commonly used methods which are mathematical programming models and heuristics algorithms. Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain. A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between two machines. The objectives of this research are to build a suitable mathematical model capable of minimizing the maximum completion time, to propose a heuristic optimization algorithm to solve the problem, and to develop an applicable program of the heuristics algorithm. A Mixed Integer Programming (MIP) model and a heuristics optimization algorithm was developed and tested using a randomly generated data set for feasibility. The overall results and performance of each approach was compared between the two methods that would assist the decision maker in choosing a suitable solution for their manufacturing line.

Modified Marine Predators Algorithm hybridized with teaching-learning mechanism for solving optimization problems.

Marine Predators Algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Lévy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.

Модель и параметрическая оптимизация проактивной защиты сервиса электронной почты от сетевой разведки

The purpose of the study: to increase the security of the e-mail service of information systems in the conditions of network intelligence. Methods used: methods of mathematical statistics, random processes research, mathematical programming, heuristic optimization algorithms were used to achieve the research goal. The result of the study: a semi-Markov model of proactive protection of the e-mail service from network intelligence has been developed, which allows determining the probabilistic and temporal characteristics of the process of transmitting e-mail messages. Based on traffic analysis, statistical hypotheses about the types of distributions of the time of occurrence of events, under the influence of which the system under study evolves in a discrete set of states, are verified, point and interval estimates of the values of the parameters of these distributions are performed. The solution of the system of linear integral Volterra equations with integral kernels of the difference type was carried out using numerical methods of the Laplace transform. The problem of vector optimization is solved to determine the optimal parameters for configuring e-mail messages, allowing to maximize the effectiveness of the protection of the e-mail service, the robustness of the simulated system and minimize overhead costs under appropriate restrictions. The extremum of the objective functions was found using a bioinspired particle swarm algorithm. Scalarization of Pareto-optimal estimates was carried out using the ideal point method. Scientific novelty: it consists in developing a model and solving the problem of optimizing the parameters of the e-mail service in the conditions of network intelligence using the mathematical apparatus of semi-Markov processes, numerical methods of Laplace transformation, parametric evaluation of statistical characteristics of the model, scalarization of a multi-criteria optimization problem by the ideal point method and search for the extremum of objective functions using the particle swarm algorithm.

Sand Cat Swarm Optimization Based on Stochastic Variation With Elite Collaboration

The sand cat swarm optimization (SCSO) is a new heuristic optimization algorithm that simulates the behavior of sand cat groups in the desert using hearing to hunt. To address the shortcomings of low accuracy of SCSO solution, slow convergence in late iterations and easy convergence stagnation, a sand cat swarm optimization based on stochastic variation and elite collaboration (SE-SCSO) was proposed. SE-SCSO first introduces a nonlinear periodic adjustment mechanism to balance the exploration and local exploitation ability of the algorithm and accelerate the convergence of the algorithm.Secondly, the pseudo-opposition and pseudo-reflection learning mechanisms are used to speed up the optimization-seeking efficiency of the SCSO algorithm and improve the global convergence capability. Designing elite collaborative strategies with random variation to enable the algorithm to jump away from the local extrema, further improving the algorithm’s optimization-seeking accuracy and convergence speed. In the simulation experiments, SE-SCSO is compared with Sand Cat Swarm Optimization (SCSO), Sine Cosine Algorithm (CSA), Circle Search Algorithm (SCA), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Golden Jackal Optimization (GJO) are tested for comparison. The experimental results validate the effectiveness of the proposed improvement strategies. Finally, SE-SCSO is applied to three engineering optimization problems. The results show that the improved strategy can effectively improve the performance performance of the algorithm, which gives SE-SCSO the advantages of high convergence accuracy, fast convergence, and the ability to jump out of local optimal solutions.

Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

Determining the optimal strategy for aggregate production planning (APP) during the Covid-19 crisis using an algorithm Harmony Search Algorithm

Reliability analysis and ABC based optimization for CoMP-enabled systems over Nakagami-[formula omitted] fading

Providing reliable communications has become one of the major goals for machine-type-oriented applications. In this article, reliable analysis and joint optimization over Nakagami-m fading are investigated for coordinated multi-point (CoMP) assisted multi-cell systems. First, the reliability analysis model for user equipment (UE) served by multiple base stations (BSs) via CoMP technique over Nakagami-m fading is presented. The exact closed-form expression for reliability estimation based on the received signal-to-interference-plus-noise ratio (SINR) value over Nakagami-m fading is derived and verified. Then, the joint sub-carrier assignment and power allocation problem for reliability optimization is formulated. The formulated problem is proved to be NP-hard. Bio-inspired artificial bee colony algorithm (ABC) is thus invoked to tackle this problem, and three ABC based joint optimization frameworks, namely Two-Step ABC Optimization algorithm (TSABC), ABC Combinatorial Optimization algorithm (ABCCO), and Heuristic Two-Step Optimization algorithm (HTSO), are proposed. Simulation results show that the UE reliability can be significantly enhanced by these proposed frameworks. It is also showcased that the proposed ABCCO obtains optimized reliability of 16 nines within 700 generations for most scenarios, which outperforms TSABC, HTSO, and conventional genetic algorithm (GA).

Heuristic approach for the multiobjective optimization of the customized bus scheduling problem

Customized bus is a typical demand-responsive transit (DRT) service that allows passengers to customize their bus route on demand and be picked up and dropped off at their desired location. A set of ride requests sent at various times by geographically dispersed passengers are operated in real time by the controller and then assigned to the shared bus with the assurance of maintaining the minimum vehicle and driver costs, which introduces a new challenge to scheduling. In this paper, a mixed integer linear programming (MILP) model is presented to solve the customized bus scheduling problem. To quickly generate satisfactory scheduling solutions in the case of single- and multi-depots, a heuristic multi-objective optimization algorithm based on preemptive scheduling is proposed, which can converge to the optimal solution among a set of vehicle blocks (consecutive trips by one bus). A real-world customized bus scheduling problem from the public transport company of Chengdu, China that contains 2,000-trip instances operated on a day is used to demonstrate that the proposed approach reduces total operating costs by 30.51% and 30.71%, compared with the simulated annealing algorithm and genetic algorithm, respectively.

Simultaneous identification of groundwater contaminant source and simulation model parameters based on an ensemble Kalman filter – Adaptive step length ant colony optimization algorithm

The simulation–optimization (SO) method is the most widely used method for groundwater contaminant source identification. However, the heuristic optimization algorithms in the SO method often rely on the selection of the initial point (the initial estimate of each variable to be solved). If the initial point is far from the actual value, the convergence speed of the algorithm will be slow, and it will easily converge prematurely. It is challenging to obtain the accurate identification results of the contaminant source. Therefore, providing a good initial point for the optimization algorithm is critical. The ensemble Kalman filter (EnKF) algorithm has the advantages of a simple calculation process and fast running speed. However, when the inverse problem has a strong nonlinearity, its identification accuracy still needs improvement. This study combined the EnKF and adaptive step length ant colony optimization (ASACO) algorithms to construct an EnKF-ASACO algorithm. This was used to identify pollution source characteristics and simulation model parameters simultaneously. First, the EnKF algorithm was adopted to provide a good initial point for the optimization algorithm. On this basis, the adaptive step length search strategy was used to improve the ant colony optimization (ACO) algorithm, and the ASACO algorithm was obtained. As a result, the step length changed adaptively to avoid falling into the local optimum. At the same time, to reduce the substantial computational load caused by repeatedly calling the numerical simulation model, the Kriging method was used to establish a surrogate model of the simulation model. Finally, the identification results of the EnKF-ASACO algorithm were compared and analyzed with those of the EnKF, previous ACO, and ASACO algorithms to verify the effectiveness of the proposed new algorithm. The results show that compared with the three single algorithms, the EnKF-ASACO algorithm constructed in this study can speed up the convergence speed and significantly improve the search efficiency and identification accuracy. Additionally, the EnKF-ASACO algorithm was also applied in the scenario with measurement errors, and its robustness and reliability were further verified.

Power system operation enhancement using a new hybrid methodology for optimal allocation of FACTS devices

The proposed research article introduces a new hybrid heuristic algorithm based on Autonomous Groups Particle Swarm and Grey Wolf optimizers (AGPSO-GWO) via multi-objective function for the allocation of the Flexible Alternating Current Transmission Systems (FACTS) controllers in power grids to minimize the active power system losses, voltage deviation, and the system operational costs. In this research work, the Static Var Compensator (SVC), Thyristor-Controlled Series Compensator (TCSC), and Unified power flow controller (UPFC) are used as FACTS controllers. A comparative analysis is conducted with other proposed heuristic optimization algorithms such as Particle Swarm Optimization (PSO), Autonomous Group Particle Swarm Optimization (AGPSO) Grey Wolf optimizer (GWO), and improved grey wolf optimization (IGWO) algorithms to confirm and validate the superiority of the proposed technique. Moreover, the proposed AGPSO-GWO has been compared with the previous existing Simulated Annealing (SA) in the literature to prove its superiority and ensure its validity. The proposed scheme has been validated and applied on 30-bus and 118-bus IEEE electric power systems. The numerical results had been carried out using MATLAB and MATPOWER packages. The simulation results show that the proposed algorithm offers the best performance among all algorithms to achieve the optimum global minimum solutions with the highest convergence rate.

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

Robust Control for Non-Minimum Phase Systems with Actuator Faults: Application to Aircraft Longitudinal Flight Control

This study is concerned with developing a robust tracking control system that merges the optimal control theory with fractional-order-based control and the heuristic optimization algorithms into a single framework for the non-minimum phase pitch angle dynamics of Boeing 747 aircraft. The main control objective is to deal with the non-minimum phase nature of the aircraft pitching-up action, which is used to increase the altitude. The fractional-order integral controller (FIC) is implemented in the control loop as a pre-compensator to compensate for the non-minimum phase effect. Then, the linear quadratic regulator (LQR) is introduced as an optimal feedback controller to this augmented model ensuring the minimum phase to create an efficient, robust, and stable closed-loop control system. The control problem is formulated in a single objective optimization framework and solved for an optimal feedback gain together with pre-compensator parameters according to an error index and heuristic optimization constraints. The fractional-order integral pre-compensator is replaced by a fractional-order derivative pre-compensator in the proposed structure for comparison in terms of handling the non-minimum phase limitations, the magnitude of gain, phase-margin, and time-response specifications. To further verify the effectiveness of the proposed approach, the LQR-FIC controller is compared with the pole placement controller as a full-state feedback controller that has been successfully applied to control aircraft dynamics in terms of time and frequency domains. The performance, robustness, and internal stability characteristics of the proposed control strategy are validated by simulation studies carried out for flight conditions of fault-free, 50%, and 80% losses of actuator effectiveness.

Flutter Derivatives Identification and Uncertainty Quantification for Bridge Decks Based on the Artificial Bee Colony Algorithm and Bootstrap Technique

This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell’s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model.

Efficient and Secure Remote Health Management in Cloud in Vehicular Adhoc Network Environment

Vehicle Ad Hoc Networks (VANETs) is a crucial communications framework for transferring messages between any healthcare systems. The dilemma of fixing the safest efficient route is a tedious issue in VANET. Hence the secure and most reliable way will give the appropriate solution for the routing issues in the VANET. In this paper, by using the Multi-Objective Bio-inspired Heuristic Cuckoo Search Node optimization algorithm is designed to find the efficient safest route for transferring health data within a short period. After seeing the efficient route, the node can be distinguished upon the traffic and security by using the Stochastic Discriminant Random Forest Node Classifier. Then in the selected route, the nodal distance can be calculated by applying the delay-based weighted end-to-end approach for traffic analysis. Then the authentic vehicle node can be analyzed through the Trust Aware extreme Gradient Boosting Node Classification based Secured Routing (TAXGBNC-SR) Technique. The obtained information that can be stored in the cloud. It deal with the multiple number of tasks gives to the ARM micro-controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of Things technology (Iot).

Locating critical slip surfaces of soil slopes with heuristic algorithms: A comparative study

Calculating the minimum safety factor for locating or investigating the critical slip surfaces of soil slopes is a complex optimization problem. In this work, a series of variables are utilized to represent the slip surfaces of the slope, and the Morgenstern and Price's method is used to calculate the safety factor of a given slip surface. The performances of eight heuristic optimization algorithms (grey wolf optimizer, particle swarm optimization algorithm, whale optimization algorithm, salp swarm algorithm, multi-verse optimizer, ant lion optimizer, cuckoo search algorithm, and equilibrium optimizer) for locating the critical slip surfaces of slopes are compared. Three cases (one homogeneous slope and two multi-layered slopes) show that the equilibrium optimizer is superior to the other algorithms in terms of the quality of the solution, the convergence rate, and the robustness. One should carefully use the salp swarm algorithm and ant lion optimizer to locate the critical slip surfaces of slopes.