heuristic algorithms
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Journal of Electrical and Computer Engineering Innovations, Volume:13 Issue: 1, Winter-Spring 2025, PP 1 -12Background and ObjectivesAccording 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.MethodsThe 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.ResultsUtilizing 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.ConclusionThis 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.Keywords: Autonomous Underwater Vehicles, Path Planning, Heuristic Algorithms, Optimization, Stability Analysis
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Journal of Artificial Intelligence in Electrical Engineering, Volume:12 Issue: 46, Summer 2023, PP 47 -59
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.
Keywords: Software Defined Network, Controller Placement, Honey Badger Algorithm, Heuristic Algorithms, Genetic Operators -
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.Keywords: Wireless body area networks (WBANs), Clustering, routing, heuristic algorithms, Genetic Algorithm
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Journal of Electrical and Computer Engineering Innovations, Volume:8 Issue: 1, Winter-Spring 2020, PP 125 -134Background and Objectives
According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance.
MethodsThe 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.
ResultsThe 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.
ConclusionThis 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.
Keywords: Ensemble Classifier, Heuristic Algorithms, Multi-Objective Inclined Planes, optimization algorithm, Optimal Level, Stability -
در این مقاله، تخصیص توام توان و زیرحامل در یک سیستم چندپخشی مبتنی بر MIMO-OFDM به منظور افزایش ظرفیت کلی سیستم پیشنهاد می گردد. همچنین، موضوع تخصیص عادلانه ی منابع بین گروه های چندپخشی نیز مطرح می گردد. برای رسیدن به این اهداف، یک الگوریتم تخصیص منابع زیربهینه پیشنهاد می گردد. الگوریتم پیشنهادی، علاوه بر اینکه پیچیدگی محاسباتی کمی دارد، سطح مطلوبی از منابع را به تمامی کاربران موجود در گروه های چندپخشی تخصیص می دهد. علاوه بر این، یک الگوریتم ترکیبی ژنتیک و ازدحام ذرات برای تخصیص توام توان و زیرحامل بین گرو های چندپخشی پیشنهاد داده می شود. نتایج حاصل از شبیه سازی نشان می دهد که روش های ارائه شده، ظرفیت کلی سیستم را نسبت به روش های پیشین افزایش می دهد.
کلید واژگان: الگوریتم های ابتکاری، تخصیص عادلانه ی منابع، سیستم چندپخشی، سیستم MIMO-OFDM، ظرفیتJournal of Iranian Association of Electrical and Electronics Engineers, Volume:17 Issue: 1, 2020, PP 73 -81In this paper, power and sub-carrier allocation in a MIMO-OFDM based multicast system are jointly proposed to increase the system capacity. The issue of fair allocation of the resources between the multicast groups is also discussed. To achieve these goals, a sub-optimal allocation algorithm is proposed. The proposed algorithm, in addition to have a low computational complexity, allocates a certain amount of resources to all users in the multicast group. In addition, an optimal combination of the genetic and particle swarm optimization algorithms is proposed for joint sub-carrier and power allocation between the multicast groups. The simulation results show that the proposed methods increase the total system capacity compared to the previous ones.
Keywords: Heuristic algorithms, Fairness resource allocation, Multicast system, MIMO-OFDM system, Capacity -
Journal of Operation and Automation in Power Engineering، سال هشتم شماره 1 (Winter-Spring 2020)، صص 57 -64
امروزه از الگوریتم های جمعیتی مبتنی بر تصادف جهت بهینه یابی استفاده گستردهای میشود . دسته مهمی از این الگوریتم ها با ایده گرفتن از فرایندهای فیزیکی یا رفتارهای موجودات به وجود آمدهاند . این مقاله ارائه دهنده یک روش جدید جهت دستیابی به جوابهای شبه بهینه مربوط به مسائل بهینه سازی در علوم مختلف است. در این مقاله رویکرد جدید استفاده از روابط اجتماعی بین افراد در یک جامعه جهت بهینه سازی بررسی شده است. در الگوریتم پیشنهادی عامل های جستجوگر، افراد یک جامعه هستند که با پیروی کردن از یکدیگر سعی در پیشرفت جامعه دارند. روش پیشنهادی یا برخی از روش های جستجوی ابتکاری مقایسه شده است. نتایج ارایه شده عملکرد مناسب آن را در بهینه یابی نشان می دهد.
کلید واژگان: بهینه یابی، الگوریتم های هیوریستیک، الگو، بهینه یابی پیرویJournal of Operation and Automation in Power Engineering, Volume:8 Issue: 1, Winter-Spring 2020, PP 57 -64These 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.
Keywords: optimization, social relationships, heuristic algorithms, following optimization, following -
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.Keywords: orientation search algorithm, Heuristic algorithms, optimization, orientation, orientation game
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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.Keywords: Redundancy allocation problem, k, out, of, n systems, Meta, heuristic algorithms, Simulation methods, Enterprise Dynamic (ED) software
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یکی از چالش های اصلی در استفاده از سیستم های فازی، چگونگی طراحی پایگاه قواعد فازی با پارامترهای بهینه سازی شده است؛ به نحوی که منجر به عملکرد رضایت بخش سیستم شود. در این مقاله از روش آموزش ترکیبی تکامل تفاضلی مبتنی بر تضاد(ODE) و بهینه سازی انبوه ذرات (PSO) به منظور بهینه سازی پارامتر های توابع عضویت گوسی پایگاه قواعد در سیستم فازی نوع تاکاگی – سوگنو - کانگ (TSK) استفاده شده است. همچنین از الگوریتم ترکیبی پیشنهادی، برای آموزش سیستم فازی TSK مرتبه صفر به منظور کنترل دو پلنت غیرخطی استفاده شده است و نتایج به دست آمده بیانگر این است که برای کنترل پلنت های غیرخطی مدل، دقت شناسایی بهتری را نسبت به سایر رویکرد های آموزشی از خود نشان می دهد. همچنین در این مقاله از ترکیب الگوریتم های ODE و PSO استفاده شده است و آن را در دو مسئله طراحی سیستم فازی دقت گرابه کار می گیرد. در این دو مدل، همه پارامترهای آزاد سیستم فازی TSK مرتبه یک، ازطریقHODEPSO بهینه می شوند. مدل های استفاده شده در این آزمایش ها، سری آشوبناک مکی گلس و یک مسئله اقتصادی واقعی هستند که مقادیر آینده آن ها پیش بینی می شود. نتایج به دست آمده بیانگر آن است که HODEPSO حداقل خطای متوسط تست و آموزش را در مقایسه با دیگر روش های آموزش دارد.کلید واژگان: الگوریتم های فراابتکاری تکامل تفاضلی مبتنی بر تضاد (ODE)، بهینه سازی انبوه ذرات (PSO)، سیستم های فازی، هوش جمعی (SI)Fuzzy systems are a useful means that are applied to various problems, including decision making, taxonomy, modeling, prediction, and control. The major challenge in using such systems is designing a fuzzy rule base with optimized parameters to maintain a desirable system performance. In this paper, a hybrid particle swarm optimization and opposition-based differential evolution training method is proposed and used to optimize the Gaussian membership function parameters of the rule base in a fuzzy system of type Takagi-Sugeno-Kang (TSK). In this dissertation, the effect of soft computing methods, e.g. evolution computing, on a zero-order TSK fuzzy system is investigated to control two non-linear plants. This paper considers a hybrid computing approach consisting of: opposition-based differential evolution (ODE) and particle swarm optimization (PSO). Results of training a zero-level TSK fuzzy system used to control two non-linear plants indicate that the proposed hybrid algorithm has a better classification accuracy in comparison to other training approaches. Moreover, this study uses heuristic opposition-based differential evolution (ODE) and particle swarm optimization (PSO) algorithms (HODEPSO) and applies them to two accuracy-oriented fuzzy system (FS) design problems. For these two models, all free parameters of a first-level Takagi-Sugeno-Kang (TSK) system are also optimized using the HODEPSO algorithm. The models used in our experiments are the Mackey Glass chaos time series and a real-world economic problem whose future values are predicted using the proposed algorithm. Finally, results of these experiments also show that HODEPSO has the minimum average training and test error in comparison to other training methods.Keywords: Hybrid Training, Heuristic Algorithms, Fuzzy Membership function Optimization, Cooperative Evolution, Evolutionary Fuzzy Systems, Social Intelligence (SI), Accuracy-based Fuzzy Systems
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Different ways of data classification have been presented. Of these, different neural networks can be used, which have been highly regarded because of their proper design and the least error, if properly designed; Also when optimization process is done by heuristic algorithms Also when optimization process is done by heuristic algorithms, the effect of slow convergence and been trapped at local optimum is enhanced than other training methods such as back propagation. In this paper, a MLP neural network is trained with continuous ACO to find the hyperplanes for classifying three reference datasets. The results are shown in comparison with the back propagation algorithm. Also the mean square error is calculated to show the accuracy of the proposed methodKeywords: Heuristic algorithms, continuous ant colony algorithm, optimization, neural network, classification
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The Harmony Search (HS) algorithm is a popular metaheuristic optimization method that reproduces the music improvisation process in searching for a perfect state of harmony. HS has a remarkable ability in detecting near global optima at low computational cost but may be ine ective in performing local search. This study presents the Multi- Adaptive Harmony Search (MAHS) algorithm for sizing optimization of skeletal structures with continuous or discrete design variables. The main di erence between the proposed algorithm and classic HS is the way of choosing and adjusting the bandwidth distance (bw). Furthermore, MAHS dynamically updates the Harmony Memory Consideration ate (HMCR) and Pitch-Adjusting Rate (PAR) parameters during the search process. The robustness and performance of the MAHS algorithm are evaluated in comparison with literature, and in particular, with well-known HS variants such as Global-best Harmony Search (GHS), Self-Adaptive Harmony Search (SAHS), and Ecient Harmony Search (EHS). Optimization results obtained by the MAHS algorithm con rm the validity of the proposed approach.Keywords: Meta, heuristic algorithms, Multi, adaptive harmony search, Harmony search variants, Size optimization, Truss optimization, Frame optimization
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Scientia Iranica, Volume:21 Issue: 3, 2014, PP 1072 -1082
In general, reliability is the ability of a system to perform and maintain its functions in routine, as well as hostile or unexpected, circumstances. The Redundancy Allocation Problem (RAP) is a combinatorial problem which maximizes system reliability by discrete simultaneous selection from available components. The main purpose of this study is to develop an e ective approach to solve RAP, expeditiously. In this study, the basic assumption is considering Erlang distribution density for component failure rates. Another assumption is that each subsystem can have one of coldstandby or active redundancy strategies. The RAP is a NP-Hard problem which cannot be solved in reasonable time using exact optimization techniques. Therefore, an approach that combines an Ant Colony Optimization (ACO) algorithm as a meta-heuristic phase, and three other heuristics, is used to develop a solving methodology for RAP. Finally, to prove the eciency of the proposed approach, some well-known benchmarks in the literature are solved and discussed in detail.
Keywords: Reliability optimization, Redundancy allocation, Series, parallel system, Ant colony optimization, Heuristic algorithms, Parameter design, Taguchi approach -
در حال حاضر، مسیریابی انرژی آگاه، یکی از مهم ترین زمینه های تحقیقاتی در شبکه های حسگر بی سیم محسوب می گردد. افزایش طول عمر شبکه، چالش انگیزترین نیاز در این نوع شبکه هاست. هدف این تحقیق، معرفی الگوریتم جست وجوی هارمونی، به عنوان یک الگوریتم فرااکتشافی موفق برای مسیریابی در شبکه های حسگر بی سیم، در راستای افزایش طول عمر در این نوع شبکه هاست. برای این منظور، در سفارشی کردن این الگوریتم برای مسیریابی، دو معیار کاهش مصرف انرژی و توزیع مناسب مصرف انرژی بین گره های حسگر که منجر به افزایش طول عمر شبکه می شوند، در نظر گرفته شده است. نتایج شبیه سازی ها، توانایی این الگوریتم را در یافتن مسیر بهینه و برقراری توازن مناسب بین دو معیار ذکرشده، به خوبی نشان می دهد. همچنین جهت مقایسه روش پیشنهادی با دیگر روش ها در شرایط کاملا یکسان، یک پیاده سازی با الگوریتم ژنتیک انجام شده است. نتایج مقایسات، ناظر بر عملکرد بهتر الگوریتم جست وجوی هارمونی، نسبت به الگوریتم ژنتیک، در افزایش طول عمر شبکه است.
کلید واژگان: شبکه های حسگر بی سیم، مسیریابی انرژی آگاه، الگوریتم های فرااکتشافی، الگوریتم جست وجوی هارمونی، الگوریتم ژنتیکAt the present، energy aware routing is one of the most important research topics in wireless sensor networks. Increasing network lifetime is the most challenging need in these networks. In this study، in order to increase network lifetime، we aim to introduce a harmony search algorithm as a successful meta-heuristic algorithm for routing in the wireless sensor networks. In customizing the algorithm for routing، two following criteria، which result in increasing network lifetime، have been considered: reducing energy consumption and proper distribution of energy consumption between sensor nodes. Simulation results clearly showed the ability of this algorithm in finding the optimum route and proper balance between the abovementioned criteria. Moreover، to compare the recommended method with other methods in identical conditions، an implementation of a genetic algorithm has been carried out. Results showed the better performance of harmony search algorithm in increasing network lifetime in compared with genetic algorithmKeywords: Wireless sensor networks, Energy aware routing, Meta, heuristic algorithms, Harmony search algorithm, Genetic algorithm
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