A metaheuristic optimization algorithm for multimodal benchmark function in a GPU architecture

It is well known that the numerical solution of evolutionary systems and problems based on topological design requires a high computational power. In the last years, many parallel algorithms have been developed in order to improve its performance. Among them, genetic algorithms (GAs) are one of the most popular metaheuristic algorithms inspired by Darwin´s evolution theory. From the High Performance Computing (HPC) point of view, the CUDA environment is probably the parallel computing platform and programming model that more heyday has had in recent years, mainly due to the low acquisition cost of graphics processing units (GPUs) compared to a cluster with similar functional characteristics. Consequently, the number of GPU-CUDAs present in the top 500 fastest supercomputers in the world is constantly growing. In this paper, a numerical algorithm developed in the NVIDIA CUDA platform capable of solving classical optimization functions usually employed as benchmarks is presented. The obtained results demonstrate that GPUs are a valuable tool for acceleration of GAs and may enable its use in much complex problems. Also, a sensitivity analysis is carried out in order to show the relative weight of each GA operator in the whole computational cost of the algorithm.Fil: Mroginski, Javier Luis. Universidad Nacional del Nordeste. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Castro, Hugo Guillermo. Universidad Tecnológica Nacional. Facultad Reg. Resistencia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentin

A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results

Benchmark for Tuning Metaheuristic Optimization Technique to Optimize Traffic Light Signals Timing

Traffic congestion at intersections is an international problem in the cities. This problem causes more waiting time, air pollution, petrol consumption, stress of people and healthy problems. Against this background, this research presents a benchmark iterative approach for optimal use of the metaheuristic optimization techniques to optimize the traffic light signals timing problem. A good control of the traffic light signals timing on road networks may help in solving the traffic congestion problems. The aim of this research is to identify the most suitable metaheuristic optimization technique to optimize the traffic light signals timing problem, thus reducing average travel time (ATT) for each vehicle, waiting time, petrol consumption by vehicles and air pollution to the lowest possible level/degree. The central part of Nablus road network has a huge traffic congestion at the traffic light signals. It was selected as a research case study and was represented by the SUMO simulator. The researcher used a random algorithm and three different metaheuristic optimization techniques: three types of Genetic Algorithm (GA), Particle Swarm Algorithm (PS) and five types of Tabu Search Algorithm (TS). Parameters in each metaheuristic algorithm affect the efficiency of the algorithm in finding the optimal solutions. The best values of these parameters are difficult to be determined; their values were assumed in the previous traffic light signals timing optimization research. The efficiency of the metaheuristic algorithm cannot be ascertained of being good or bad. Therefore, the values of these parameters need a tuning process but this cannot be done by using SUMO simulator because of its heavy computation. The researcher used a benchmark iterative approach to tune the values of them etaheuristic algorithm parameters by using a benchmark function. The chosen function has similar characteristics to the traffic light signals timing problem. Then, through the use of this approach, the researcher arrived at the optimal use of the metaheuristic optimization algorithms to optimize traffic light signals timing problem. The efficiency of each metaheuristic optimization algorithm, tested in this research, is in finding the optimal or near optimal solution after using the benchmark iterative approach. The results of metaheuristic optimization algorithm improved at some values of the tuned parameters. The researcher validated the research results by comparing average results of the metaheuristic algorithms, used in solving the traffic light signals optimization problem after using benchmark iterative approach, with the average results of the same metaheuristic algorithms used before using the benchmark iterative approach; they were also compared with the results of Webster, HCM methods and SYNCHRO simulator. In the light of these study findings, the researcher recommends trying the benchmark iterative approach to get ore efficient solutions which are very close to the optimal solution for the traffic light signals timing optimization problem and many complex practical optimization problems that we face in real life.الازدحامات المرورية عند التقاطعات هي مشكله عالمية في المدن. هذه المشكلة تسبب المزيد من وقت االنتظار وتلوث الهواء و استهالك الوقود، و توتر الناس و مشاكل صحية. على هذه الخلفية، يقدم هذا البحث نهج المعيار المكرر لالستخدام تقنيات التحسين التخمينية في تحسين مشكلة توقيت اإلشارات الضوئية. التحكم الجيد في توقيت االشارات الضوئية على شبكات الطرق قد يساعد في حل مشاكل االزدحام المروري. يهدف هذا البحث الى تحديد أفضل و أنسب تقنية تحسين تخمينية لتحسين مشكلة توقيت االشارات الضوئية، وبالتالي تقليل متوسط الوقت الذي يستغرقه السفر (ATT(لكل مركبة، و وقت االنتظار، و استهالك الوقود المستخدم في المركبات و تلوث الهواء إلى أدنى مستوى ممكن. يعاني الجزء المركزي من شبكة طرق مدينة نابلس من ازدحام مروري كبير على االشارات الضوئية. و تم اختيار هذا الجزء كحالة البحث الدراسية و التي تم تمثيلها باستخدام برنامج المحاكاة سومو. و استخدم الباحث خوارزمية عشوائية و ثالث تقنيات تحسين تخمينية و هي: ثالث انواع من الخوارزمية الجينية، و خورزمية سرب الجسيمات، و خمسة انواع من خوارزمية التابو. و هناك متغيرات في كل خوارزمية تخمينية تؤثر على فعالية الخوارمية في ايجاد الحلول المثلى. و من الصعب تحديد افضل القيم لهذه المتغيرات؛ و قيم هذه المتغيرات كانت تفترض في ابحات تحسين توقيت االشارات الضوئية السابقة. وفي هذه الحاله فعالية اقتران التحسين التخميني ال يمكن التحقق منها اذا ما كانت جيده او سيئة. ولذلك فان قيم هذه المتغيرات بحاجه لعملية ضبط ، ولكن ال يمكننا ذلك باستخدام برنامج المحاكاه سومو النه حساباته ثقيله و طويله. استخدم الباحث طريقة مقارنة الدوال لضبط قيم متغيرات خوارزمية التحسين التخمينية باستخدام خوارزمية معيار. خوارمية المعيار المختاره لها خصائص شبيهه بمشكلة توقيت االشارات الضوئية. ثم من خالل استخدام هذه الطريقة، وصل الباحث الى افضل استخدام لخوارزميات التحسين التخمينية لتحسين مشكلة توقيت االشارات االضوئية. وفي هذا البحث تم اختبار فعالية كل خوارمية تحسين تخمينية في ايجاد الحل االمثل او حل قريب من الحل االمثل بعد ضبط خوارزمية التحسين التخمينية. لقد تحسنت نتائج خوارزمية التحسين التخمينية عند بعض قيم المتغيرات التي تم ضبطها. قام الباحث بالتحقق من نتائج البحث بمقارنة معدل نتائج خوارزميات التحسين التخمينية التي امستخدمها في تحسين مشكلة توقيت االشارات الضوئية قبل ضبط خوارزمية التحسين التخمينية، مع معدل نتائج نفس الخوارزميات التخمينية التي امستخدمها بعد ضبط خوارزمية التحسين التخمينية؛ وهذه النتائج تمت مقارنتها مع نتائج طريقتي ويبستر و HCM و برنامج السنكرو. في ضوء نتائج هذه الدراسة، يوصي الباحث بتجريب طريقة مقارنة الدوال لضبط خوارزميات التحسين التخمينية للحصول على حلول فعالة اكثر و التي تكون قريبة جدا من الحل االمثل لتحسين مشكلة توقيت االشارات الضوئية و لتحسين المشاكل العملية المعقدة التي تواجهنا في الحياة العملية

Harmony Search Method: Theory and Applications

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem

Study Of EMG Feature Selection For Hand Motions Classification

In recent days, electromyography (EMG) pattern recognition has becoming one of the major interests in rehabilitation area. However, EMG feature set normally consists of relevant, redundant and irrelevant features. To achieve high classification performance, the selection of potential features is critically important. Thus, this paper employs two recent feature selection methods namely competitive binary gray wolf optimizer (CBGWO) and modified binary tree growth algorithm (MBTGA) to evaluate the most informative EMG feature subset for efficient classification. The experimental results show that CBGWO and MBTGA are not only improves the classification performance, but also reduces the number of features

Battle Royale Optimizer for solving binary optimization problems

Battle Royale Optimizer (BRO) is a recently proposed metaheuristic optimization algorithm used only in continuous problem spaces. The BinBRO is a binary version of BRO. The BinBRO algorithm employs a differential expression, which utilizes a dissimilarity measure between binary vectors instead of a vector subtraction operator, used in the original BRO algorithm to find the nearest neighbor. To evaluate BinBRO, we applied it to two popular benchmark datasets: the uncapacitated facility location problem (UFLP) and the maximum-cut (Max-Cut) graph problems from OR-Library. An open-source MATLAB implementation of BinBRO is available on CodeOcean and GitHub websites.Publisher's Versio