heuristic algorithms

According to this fact that a typical autonomous underwater vehicle consumes energy for rotating, smoothing the path in the process of path planning will be especially important. Moreover, given the inherent randomness of heuristic algorithms, stability analysis of heuristic path planners assumes paramount importance. The novelty of this paper is to provide an optimal and smooth path for autonomous underwater vehicles in two steps by using two heuristic optimization algorithms called Inclined Planes system Optimization algorithm and genetic algorithm; after finding the optimal path by Inclined Planes system Optimization algorithm in the first step, the genetic algorithm is employed to smooth the path in the second step. Another novelty of this paper is the stability analysis of the proposed heuristic path planner according to the stochastic nature of these algorithms. In this way, a two-level factorial design is employed to attain the stability goals of this research. Utilizing a Genetic algorithm in the second step of path planning offers two advantages; it smooths the initially discovered path, which not only reduces the energy consumption of the autonomous underwater vehicle but also shortens the path length compared to the one obtained by the Inclined Planes system optimization algorithm. Moreover, stability analysis helps identify important factors and their interactions within the defined objective function. This proposed hybrid method has implemented for three different maps; 36.77%, 48.77%, and 50.17% improvements in the length of the path are observed in the three supposed maps while smoothing the path helps robots to save energy. These results confirm the advantage of the proposed process for finding optimal and smooth paths for autonomous underwater vehicles. Due to the stability results, one can discover the magnitude and direction of important factors and the regression model.

Software-defined networking (SDN) is a network structure where the control and data planes are separated. In traditional SDN, a single controller was in charge of control management, but this architecture had several constraints. To address these constraints, it is advisable to incorporate multiple controllers in the network. Selecting the number of controllers and connecting switches to them is known as the controller placement problem (CPP). CPP is a key hurdle in enhancing SDNs. In this paper a meta-heuristic algorithm called Honey Badger Algorithm (HBA), is used to determine the optimal alignment between switches and controllers. HBA is modified using genetic operators (GHBA). The assessments are conducted with a diverse range of controllers on four prominent software-defined networks sourced from the Internet Topology Zoo and are compared to numerous algorithms in this field. It is noted that GHBA outperforms other competing algorithms in terms of end-to-end delay and energy consumption.

Wireless body area network (WBAN) is a type of wireless communication network, which consists of tiny bio-sensor nodes attached to or implanted in the human body, to continuously monitor the patient by medical staff. Energy efficient routing in WBANs is of utmost importance, as bio-sensors are highly resource-constrained. Although many heuristic- and metaheuristic-based routing protocols have been proposed for WBANs, they suffer from some drawbacks: low solution quality of heuristics and low speed of metaheuristics in online routing. To overcome these drawbacks and simultaneously benefit from the advantage of both techniques, we present an ensemble heuristic-metaheuristic protocol (called CHM) as an adjustable routing solution for WBANs. In CHM, a multi-criteria heuristic based on the residual energy, distance to sink, path loss, and history of becoming a relay node, is used to select proper cluster heads. Furthermore, a metaheuristic algorithm using a genetic algorithm is applied to automatically tune the heuristic protocol. Simulation results in MATLAB using IEEE 802.15.6 on different WBANs demonstrate the performance of the introduced CHM protocol when compared with the existing routing protocols in terms of prolonging the application-specific network lifetime definition.

Fog computing is being seen as a bridge between smart IoT devices and large scale cloud computing. It is possible to develop cloud computing services to network edge devices using Fog computing. As one of the most important services of the system, the resource allocation should always be available to achieve the goals of Fog computing. Resource allocation is the process of distributing limited available resources among applications based on predefined rules. Because the problems raised in the resource management system are NP-hard, and due to the complexity of resource allocation, heuristic algorithms are promising methods for solving the resource allocation problem. In this paper, an algorithm is proposed based on learning automata to solve this problem, which uses two learning automata: a learning automata is related to applications (LAAPP) and the other is related to Fog nodes (LAN). In this method, an application is selected from the action set of LAAPP and then, a Fog node is selected from the action set of LAN. If the requirements of deadline, response time and resources are met, then the resource will be allocated to the application. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different Fog configurations. The obtained results are compared with several existing methods in terms of the makespan, average response time, load balancing and throughput.

Despite the fact that many governments try to set rules that guarantee having resistant buildings, there are many vulnerable structures in the world. Hence, establishing earthquake relief centers is an important issue in order to control the effect of an earthquake. Iran is a country in middle east which is severely vulnerable against earthquake. Yazd is a central city in Iran. Since there is no such a study for Yazd city, this city is considered in this study.  The parcels' layer of the GIS map of Yazd city has been used as the input of the problem. Since the location allocation of relief centers is a problem with huge complexity and cannot be solved in polynomial time, Whale Optimization Algorithm (WOA) has been used to solve the problem. The Whale Optimization Algorithm or The WOA is a particle based heuristic algorithm which is suitable for solving hard problems. The main contributions of the research are modifying WOA function for the problem and designing a new method for creating whales. In order to reduce the time of reaching to the reasonable solution an innovative whale generating method has been  designed.  The results show that average distance of each parcel from its relief center is 1541 meters and the standard deviation of 114

This research describes an optimization and rejuvenation of the heat treatment process for a nickel base superalloy grade GTD111 after long-term service. The aging heat treatment variables examined in this study included primary aging temperature, primary aging time, secondary aging temperature, and secondary aging time. The resulting materials were examined using Taguchi method design of experiments to determine the resulting material hardness test and observed with the hot tensile test, scanning electron microscopy, and energy dispersive X-ray spectroscopy. The experimental results showed what happens following optimization with the heat treatment parameters of a primary aging temperature of 1120 °C, primary aging time of 3 h, secondary aging temperature of 845 °C, and secondary aging time of 24 h. The material, after rejuvenation heat treatment via optimization with γ′ particle characteristics, had a coarse square shape, spherical shape of γ′, and fine γ′ precipitate distributed on the parent phase, which affects the mechanical properties of the material. fine γ′ precipitate distributed on parent phase, which affects the mechanical properties of the material.

According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance.

The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert software programs are utilized to achieve the stability goals of this research.

The proposed approach is useful as a preprocessing method before employing heuristic ensemble classifiers; i.e., first discover optimal levels of important parameters and then apply these parameters to heuristic ensemble classifiers to attain the best results. Another significant difference between this research and previous works related to stability analysis is the definition of the response variable; an average of three criteria of the Pareto front is used as response variable.Finally, to clarify the performance of this method, obtained optimal levels are applied to a typical multi-objective heuristic ensemble classifier, and its results are compared with the results of using empirical values; obtained results indicate improvements in the proposed method.

This approach can analyze more parameters with less computational costs in comparison with previous works. This capability is one of the advantages of the proposed method The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

This study presents a mathematical approach to distribute portable excess speed detectors in urban transportation networks. This type of sensor is studied to be located in a network in order to separate most of the demand node pairs in the system resembling the well-known traffic sensor surveillance problem. However, newly, the locations are permitted to be changed introducing the dynamic form of the sensor location problem. The problem is formulated mathematically into three different location problems, namely SLP1, SLP2, and SLP3. The aim is to find the optimal number of sensors to intercept most of the daily traffic for each model objective. The proposed formulations are proven to be an NP-hard problem, and then heuristics are called for the solution. The methodology is applied to AL Riyadh city as a real case study network with 240 demand node pairs and 124 two-way streets. In the SLP1, all the demand node pairs are covered by 19% of the network’s roads, whereas SLP2 model shows the best locations for each assumed budget of sensors to purchase. The SLP2 solutions range from 24 sensors with 100% paths coverage to 1 sensor with nearly 20% of paths coverage. The SLP3 model manages to redistribute the sensors in the network while maintaining its traffic coverage efficiency. Four locations structures manage to cover all the network streets with coverage ranges between 100% and 60%. The results show the capability of providing satisfactory solutions with reasonable computing burden.

These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying optimization-based social relationships among the members of a community is investigated in this paper. In the proposed algorithm, search factors are indeed members of the community who try to improve the community by ‘following’ each other. FOA implemented on 23 well-known benchmark test functions. It is compared with eight optimization algorithms. The paper also considers for solving optimal placement of Distributed Generation (DG). The obtained results show that FOA is able to provide better results as compared to the other well-known optimization algorithms.

Random based inventive algorithms are being widely used for optimization. An important category of these algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed algorithm for optimizing the orientation game is a series of optimization algorithms that are formed with the idea of an old game and search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the game referee's orders. The best position is achieved by the laws are there in this game .In this paper, the real version of the algorithm is presented. The results of optimization of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed algorithm over the genetic algorithm and the particle swarm optimization algorithm.

Monitoring the seepage, particularly the piezometric water level in the dams, is of special importance in hydraulic engineering. In the present study, piezometric water levels in three observation piezometers at the left bank of Jiroft Dam structure (located in Kerman province, Iran) were simulated using soft computing techniques and then compared using the measured data. For this purpose, the input data, including inflow, evaporation, reservoir water level, sluice gate outflow, outflow, dam total outflow, and piezometric water level, were used. Modeling was performed using multiple linear regression method as well as soft computing methods including regression decision tree, classification decision tree, and three types of artificial neural networks (with Levenberg-Marquardt, particle swarm optimization, PSO, and harmony search learning algorithms, HS). The results of the present study indicated no absolute superiority for any of the methods over others. For the first piezometer the ANN-PSO indicates better performance (correlation coefficient, R=0.990). For the second piezometer ANN-PSO shows better results with R=0.945. For the third piezometers MLR with R=0.945 and ANN-HS with R=0.949 indicate better performance than other methods. Furthermore, Mann-Whitney statistical analysis at confidence levels of 95% and 99% indicated no significant difference in terms of the performance of the applied models used in this study.

The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a genetic algorithm (GA) is used for solving this problem. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems.

For remote places having less-strong wind, single resources based renewable energy system (RES) with battery storage can sustainably and economically generate electrical energy. There is hardly any literature on optimal sizing of such RES for very low load demand situation. The objective of this study is to techno-economically optimize the system design of a Photovoltaic (PV)-battery storage RES for an institutional academic block in Silchar, India having maximum demand less than only 30 kW. The sizing process of various subsystems of the RES is first discussed. Then the RES is techno-economically optimized under 100% reliability to power supply condition, i.e. 0% unmeet energy (UE) and least excess energy. In this, performances of three different optimization algorithms- genetic algorithm (GA) and two meta-heuristics, namely Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO) algorithms are investigated and compared. The optimal configuration under least levelized cost of energy (COE) is further examined. Results demonstrate that GWO is the best optimization tool for optimizing the cost of energy (COE) in comparison with the other optimization algorithms. It has been shown that a single optimization method might not always guarantee that the objective function has converged successfully in fulfilling all the requirements of least excess energy, autonomy days, and least COE. The present research provides a useful reference for the design optimization of single resource based RES for low load demand situation.

In this article, a new model and a novel solving method are provided to address the non-exponential redundancy allocation problem in series-parallel k-out-of-n systems with repairable components based on Optimization Via Simulation (OVS) technique. Despite the previous studies, in this model, the failure and repair times of each component were considered to have non-negative exponential distributions. This assumption makes the model closer to the reality where the majority of used components have greater chance to face a breakdown in comparison to new ones. The main objective of this research is the optimization of Mean Time to the First Failure (MTTFF) of the system via allocating the best redundant components to each subsystem. Since this objective function of the problem could not be explicitly mentioned, the simulation technique was applied to model the problem, and di erent experimental designs were produced using DOE methods. To solve the problem, some meta-Heuristic Algorithms were integrated with the simulation method. Several experiments were carried out to test the proposed approach; as a result, the proposed approach is much more real than previous models, and the near optimum solutions are also promising.

In software development and software project management, Software Cost Estimation (SCE) will be considered a major step in the start of projects. SCE is one of the main activities at the decisions of software's time and expense management which has a special status in a software project. SCE in software development is considered as a key parameter in software project management. Therefore, to achieve the basic goals requires accurate and reliable cost estimate. Actual estimate in software development is based on effective factors that its accurate value should be recognized using algorithmic models and Artificial Intelligence (AI). Boehm used COCOMO model for SCE which is an algorithmic model in 1981. Algorithmic models such as COCOMO are based on criteria such as the number of lines of code or the Function Point (FP). In COCOMO model, project development and then the cost is calculated by such units. Therefore, the lower accuracy and unreliability of the algorithmic models creates a substantial risk in software projects, so, regularly estimating the cost throughout the project is necessary and it should be compared with other techniques. In the meantime, meta-heuristic algorithms in recent years have made ??good progress in the area of ??software and it has been used widely in SCE. Among meta-heuristic algorithms, Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO) used to optimize the issues based on population and they have good effects in optimizing estimation factors. In this paper, a hybrid model DE-ACO, PSO-ACO and ABC-ACO based on ACO algorithm have been proposed for optimization based on effective factors in COCOMO model. Test results show that hybrid models have less magnitude of relative error (MRE) and Mean MRE (MMRE) in estimating software project cost in comparison with COCOMO model.