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big bang-big crunch optimization algorithm with local directional moves

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第5期21卷 1359-1375页

作者： Genc, Hakk Murat Eksin, Ibrahim Erol, Osman Kaan Istanbul Tech Univ Fac Elect & Elect Engn Dept Control Engn TR-34469 Istanbul Turkey

The big bang-big crunch (BB-BC) algorithm has been proposed as a novel optimization method that relies on one of the theories of the evolution of the universe, namely the big bang and big crunch theory. It has been shown that this algorithm is capable of quick convergence, even in long, narrow parabolic-shaped flat valleys or in the existence of several local extremes. In this work, the BB-BC optimization algorithm is hybridized with local search moves in between the "banging" and "crunching" phases of the algorithm. These 2 original phases of the BB-BC algorithm are preserved, but the representative point ("best" or "fittest" point) attained after the crunching phase of the iteration is modified with some local directional moves, using the previous representative points so far attained, with the hope that a better representative point would be obtained. The effect of imposing local directional moves is inspected by comparing the results of the original and enhanced BB-BC algorithm on various benchmark test functions. Moreover, the crunching phase of the algorithm is improved with the addition of a simplex-based local search routine. The results of the new hybridized algorithm are compared with the state-of-the-art variants of widely used evolutionary computation methods, namely genetic algorithms, covariance matrix adaptation evolutionary strategies, and particle swarm optimization. The results over the benchmark test functions have proven that the BB-BC algorithm, enhanced with local directional moves, has provided more accuracy with the same computation time or for the same number of function evaluations.

关键词： big bang-big crunch optimization algorithm evolutionary computation hybrid local search

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optimization Based Path Planning via big bang-big crunch with Local Search 5

Optimization Based Path Planning via Big Bang-Big Crunch wit...

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5th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Yilmaz, Sabri Gokasan, Metin Istanbul Tech Univ Control & Automat Engn Dept Istanbul Turkey

ISBN: (纸本)9781479982523

In this paper a search for the trajectory that minimizes the cost function is studied. In robotic studies the cost function can be defined as a function of time, tracking error or applied torque. In this study the cost function is selected as a function of applied torque, so the main aim is minimizing the energy consumption. For this purpose a simple robot manipulator is chosen, and its kinematic and dynamic models are derived by Denavit-Hartenberg convention and Euler-Lagrange method. Then two different trajectory polynomials are described, one is solved from boundary conditions without optimization and one is solved by optimization and the same boundary conditions. These two different trajectory polynomials and their cost functions values are compared. The effect and efficiency of optimization are examined.

关键词： trajectory planning robot dynamics global optimization big bang-big crunch optimization algorithm

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Optimized echo state networks using a big bang-big crunch algorithm for distance protection of series-compensated transmission lines

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 54卷 408-424页

作者： Deihimi, Ali Solat, Alireza Bu Ali Sina Univ Dept Elect Engn Hamadan *** Iran

Distance protection of transmission lines compensated by Thyristor-Controlled Series Capacitor (TCSC) suffers malfunction due to series variable voltages injected by TCSC. To mitigate the problem, echo state networks (ESNs) are used in this paper through the voltage compensation technique by subtracting estimated TCSC voltages from measured phase voltages where fault loop observed by the relay contains TCSC. The design parameters of ESNs are optimized using a big bang-big crunch (BB-BC) algorithm with an especial random generation scheme in big bang phase. Main features of our study are: (a) only locally measured phase currents at the relaying point is used, assuming firing angles and other variables of TCSC are not accessible there;and (b) high accuracy of estimations is achieved using optimized ESNs for a large number of test cases. The method is tested on a 400 km, 500 kV line for all fault types using Matlab/Simulink. Tests for 7680 cases with varying fault resistance, fault inception-angle, fault location, load angle and compensation degree show much improved accuracy of estimations by ESN in comparison to time-delay neural network (TDNN), radial basis function neural network (RBFNN), nonlinear autoregressive network with exogenous inputs (NARX) and Elman network particularly at instants immediately after fault incidence. The effectiveness of the ESN-based method is examined using two distance relay algorithms. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： big bang-big crunch optimization algorithm Distance protection Echo state network Thyristor-controlled series capacitor Time-delay neural network

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Optimal Trajectory Planning by big bang-big crunch algorithm

Optimal Trajectory Planning by Big Bang-Big Crunch Algorithm

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2nd International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Yilmaz, Sabri Goekasan, Metin Istanbul Tech Univ Control & Automat Engn Dept TR-80626 Istanbul Turkey

ISBN: (纸本)9781479967735

Path planning is an interesting topic which is affected by lots of variables, as: time, energy, torque and stability. In this study, a new method based on big bang-big crunch algorithm is proposed to find optimum values of the parameters of a path and a cost function in order to minimize applied torque and tracking error. For this purpose the mathematical model of the manipulator is derived with mainly used methods, Denavit-Hartenberg, Jacobian and Euler-Lagrange methods. By using classical robot modeling methods, big bang-big crunch algorithm searched for the optimum trajectory and found the optimum value of the cost function.

关键词： Path Planning big bang-big crunch optimization algorithm Robot Kinematics Robot Dynamics

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A stochastic neighborhood search approach for airport gate assignment problem

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第1期39卷 316-327页

作者： Genc, Hakki Murat Erol, Osman Kaan Eksin, Ibrahim Berber, Mehmet Fatih Guleryuz, Binnur Onaran Natl Res Inst Elect & Cryptol Inst Informat Technol TR-41470 Gebze Kocaeli Turkey Istanbul Tech Univ Fac Elect & Elect Engn Dept Control Engn TR-34469 Istanbul Turkey Istanbul Tech Univ Fac Elect & Elect Engn Dept Comp Engn TR-34469 Istanbul Turkey TAV Informat Technol Corp Istanbul Ataturk Int Airport TR-34149 Istanbul Turkey

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for Istanbul Ataturk Airport. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Gate assignment problem big bang-big crunch optimization algorithm

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An FPGA-Based Accelerated optimization algorithm for Real-Time Applications

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2020年第10期92卷 1155-1176页

作者： Psarakis, Mihalis Dounis, Anastasios Almabrok, Abdoalnasir Stavrinidis, Stavros Gkekas, Georgios Univ Piraeus Dept Informat Piraeus Greece Univ West Attica Dept Ind Design & Prod Engn Athens Greece

Many modern applications, such as industrial control, image processing and machine learning, are based on optimization algorithms that must solve compute-intensive problems under real-time constraints taking into account real-world parameters that evolve in time. Among others, evolutionary algorithms (EAs) are increasingly used in real-time applications to solve such complex optimization problems. Moreover, Field Programmable Gate Arrays (FPGAs) have been proved an effective platform for the implementation of these algorithms satisfying real-time and low-power requirements. In this paper, we study the FPGA-based acceleration of the big bang-big crunch (BB-BC) algorithm. BB-BC is an optimization method inspired by the corresponding evolutionary theory of the universe [1]. The BB-BC method is performed in two phases: in the Bing bang phase, similarly to other Genetic algorithms (GAs) it generates a random population of candidate solutions, while in the big crunch phase it shrinks these candidates around an optimal point. It has been shown that the BB-BC method outperforms classical GA algorithms for several optimization problems in terms of convergence speed. We show that the BB-BC algorithm does not suffer from the design limitations of the classical GAs that impede the performance of their hardware-based accelerators. We propose an efficient fully pipelined design of both BB-BC phases and a parallel scheme which integrates several BB-BC pipelined engines to improve system performance. We implement the proposed FPGA-based accelerator on a Xilinx Virtex-5 development board for three different fitness functions and compare the execution time against its software counterpart (in C language) and a CUDA program running on a massively parallel computing platform (GPU). We also compare the proposed optimized FPGA architecture with an RTL design generated by a high-level synthesis (HLS) design flow. We propose an Adaptive Neuro-fuzzy Inference System (ANFIS) model for fitnes

关键词： FPGA-based acceleration Genetic algorithm big bang-big crunch optimization algorithm Real-time applications ANFIS

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Type-2 fuzzy model based controller design for neutralization processes

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ISA TRANSACTIONS 2012年第2期51卷 277-287页

作者： Kumbasar, Tufan Eksin, Ibrahim Guzelkaya, Mujde Yesil, Engin Istanbul Tech Univ Fac Elect & Elect Engn Dept Control Engn TR-34469 Istanbul Turkey

In this study, an inverse controller based on a type-2 fuzzy model control design strategy is introduced and this main controller is embedded within an internal model control structure. Then, the overall proposed control structure is implemented in a pH neutralization experimental setup. The inverse fuzzy control signal generation is handled as an optimization problem and solved at each sampling time in an online manner. Although, inverse fuzzy model controllers may produce perfect control in perfect model match case and/or non-existence of disturbances, this open loop control would not be sufficient in the case of modeling mismatches or disturbances. Therefore, an internal model control structure is proposed to compensate these errors in order to overcome this deficiency where the basic controller is an inverse type-2 fuzzy model. This feature improves the closed-loop performance to disturbance rejection as shown through the real-time control of the pH neutralization process. Experimental results demonstrate the superiority of the inverse type-2 fuzzy model controller structure compared to the inverse type-1 fuzzy model controller and conventional control structures. (C) 2011 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Type-2 fuzzy models Inverse fuzzy model big bang-big crunch optimization algorithm Internal model control pH neutralization process

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Fast Tuning of the PID Controller in An HVAC System Using the big bang-big crunch algorithm and FPGA Technology

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algorithmS 2018年第10期11卷 146-146页

作者： Almabrok, Abdoalnasir Psarakis, Mihalis Dounis, Anastasios Univ Piraeus Dept Informat Piraeus 18534 Greece Univ West Attica Dept Ind Design & Prod Engn Piraeus 12244 Greece

This article presents a novel technique for the fast tuning of the parameters of the proportional-integral-derivative (PID) controller of a second-order heat, ventilation, and air conditioning (HVAC) system. The HVAC systems vary greatly in size, control functions and the amount of consumed energy. The optimal design and power efficiency of an HVAC system depend on how fast the integrated controller, e.g., PID controller, is adapted in the changes of the environmental conditions. In this paper, to achieve high tuning speed, we rely on a fast convergence evolution algorithm, called big bang-big crunch (BB-BC). The BB-BC algorithm is implemented, along with the PID controller, in an FPGA device, in order to further accelerate of the optimization process. The FPGA-in-the-loop (FIL) technique is used to connect the FPGA board (i.e., the PID and BB-BC subsystems) with the plant (i.e., MATLAB/Simulink models of HVAC) in order to emulate and evaluate the entire system. The experimental results demonstrate the efficiency of the proposed technique in terms of optimization accuracy and convergence speed compared with other optimization approaches for the tuning of the PID parameters: sw implementation of the BB-BC, genetic algorithm (GA), and particle swarm optimization (PSO).

关键词： big bang-big crunch optimization algorithm FPGA based acceleration digital PID controller FPGA-in-the-loop heat ventilation air condition

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Order Reduction of Interval Systems using big bang big crunch and Routh Approximation 1

Order Reduction of Interval Systems using Big Bang Big Crunc...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Tanwar, Narendra Singh Bhatt, Rajesh Parmar, Girish Rajasthan Tech Univ Dept Elect Engn Kota India

ISBN: (纸本)9781467385879

Today's requirement is a system which is easier to control and simple in nature. Hence, it is desirable to reduce these higher order systems into the lower order system. A system with constant coefficient but uncertain within finite range is known as interval system. A lot or work has been presented in the previous years based on the nature inspired techniques. This paper aims to reduce linear continuous time interval system using mixed evolutionary techniques. The Routh approximation is used to obtain denominator or higher order system using the generalized routh table. The numerator or reduced order system is obtained using big bang-big crunch optimization algorithm by minimizing integral square error between original and reduced order system. Reduced order system retains the stability and steady state value or the higher order system. The numerical examples are illustrated and results are compared with the other well known methods.

关键词： Large Scale Interval System Order Reduction Interval Arithmatics big bang-big crunch optimization algorithm Routh Approximation Integral Square Error

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Energy Efficient Trajectory Planning via big bang big crunch with Obstacle Avoidance 6

Energy Efficient Trajectory Planning via Big Bang Big Crunch...

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6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Yilmaz, Sabri Gokasan, Metin Istanbul Tech Univ Control & Automat Engn Dept Istanbul Turkey

ISBN: (纸本)9781509011780

Robot manipulation has been an interesting topic for researchers over decades. While researches are going on, different problems occurred. One of the most important problems is energy consumption. Because operation time of mobile robots is fully related with energy consumption. In this study two main problems are taken into consideration, first one is minimization of energy consumption and second one is obstacle avoidance. For this purpose, a simple robot manipulator with two degrees of freedom is chosen. The objective function is selected as a function of applied torque values at the joints, minimization of objective function, results in minimization of motor currents. The algorithm checks if there is a contact between the obstacles and the manipulator, while the algorithm is searching for the trajectory polynomial that requires minimum energy consumption. Finally, the algorithm finds the trajectory polynomial that contains no contact between the manipulator and the obstacles, also minimizes energy consumption.

关键词： trajectory planning robot dynamics global optimization big bang-big crunch optimization algorithm obstacle avoidance

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