Conventional Genetic Algorithm Research Articles

Reconfiguration of Smart Distribution Systems With Time Varying Loads Using Parallel Computing

The problem of finding optimal configuration of automated/smart power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when the time varying nature of loads is taken into account. In this paper, a systematic approach is proposed to determine an optimal long-term reconfiguration schedule. To solve the optimization problem, a novel adaptive fuzzy-based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into the proposed method enhances the efficiency of the parallel GA by adaptively modifying the migration rates among different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed, which automatically generates radial topologies and prevents the construction of infeasible radial networks in the optimization process. In order to consider the dynamic behavior of the load and reduce the load condition scenarios over the year under study, fuzzy C-mean clustering method is utilized. Finally, the performance of the proposed method is demonstrated on a 119-bus distribution network, and is compared with that of conventional single GA and conventional parallel GA.

Attack Path Selection Optimization with Adaptive Genetic Algorithms

Enterprise network security solution optimization is a complex and challenging problem given the large number of components that are involved. This paper describes our project that has the ultimate goal of providing optimized solutions for enterprise network security. We describe our approach for implementing an optimized security assessment using Genetic Algorithm (GA). Because of the dynamic nature of an enterprise network, all security analysis tools devised for the network need to function in dynamic mode as well. We have incorporated a fuzzy logic controller (FLC) to fine tune the optimization algorithm in run time. The FLC takes into consideration both the exploration and exploitation aspect of the GA. It calculates the appropriate adjustments for the control parameters such as selection and crossover rate. Possible attack paths are then identified and evaluated based on an attack graph representing the network under study. Attack paths that satisfy certain optimization criteria are then selected and presented as possible components of the security solution. We have also carried out a comparative study on the performance of the adaptive GA used in this problem against the conventional GA. The results shows that attack graphs analyzed with the adaptive GA yields significantly better solutions. © 2016 American Scientific Publishers. All rights reserved.

A genetic algorithm for supply chain configuration with new product development

New product development has become increasingly important recently due to highly competitive market place and economic reasons. Development and production of new products in the planning horizon require an efficient and responsiveness supply chain network. As new products appear in the market, the old products could become obsolete, and then phased out. A generously persuasive parameter for new product and developed product problems in a supply chain is the time which the developed products are introduced and the old products are phased out and also the time new products are introduced in the planning horizon in order to maximum the total profit.With consideration of the factors noted above, this study proposes to design a multi echelon multi product multi period supply chain model which incorporates product development and new product production and their effects on supply chain configuration.In terms of the solution technique, to overcome NP-hardness of the proposed model, priority based genetic algorithm is applied to find the suitable time for introducing developed and new product in the planning horizon, production schedule and design of supply chain network in order to maximum the total profit in a reasonable computational time. The accuracy of the proposed genetic algorithm is validated on small, medium and large instances that have been solved using the software LINGO, in order to evaluate the performance of the algorithm. Then, the implementation of the fuzzy crossover and mutation controllers is described. It is able to regulate the rates of crossover and mutation operators during the search process. Finally, a comparison is done on conventional GA and the controlled GA.

A novel adaptive feature selector for supervised classification

Optimal feature Selection is an imperative area of research in medical data mining systems. Feature selection is an important factor that boosts-up the classification accuracy. In this paper we have proposed a adaptive feature selector based on game theory and optimization approach for an investigation on the improvement of the detection accuracy and optimal feature subset selection. Particularly, the embedded Shapely Value includes two memetic operators namely include and remove features (or genes) to realize the genetic algorithm (GA) solution. The use of GA for feature selection facilitates quick improvement in the solution through a fine tune search. An extensive experimental comparison on 22 benchmark datasets (both synthetic and microarray) from UCI Machine Learning repository and Kent ridge repository of proposed method and other conventional learning methods such as Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (KNN), J48 (C4.5) and Artificial Neural Network (ANN) confirms that the proposed SVEGA strategy is effective and efficient in removing irrelevant and redundant features. We provide representative methods from each of wrapper, filter, conventional GA and show that this novel memetic algorithm – SVEGA yields overall promising results in terms of the evaluation criteria namely classification accuracy, number of selected genes, running time and other metrics over conventional feature selection methods.

A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm

During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.

An Approach for Image Fusion using PCA and Genetic Algorithm

The pattern of mixing multiple images so as to get a single, well developed image is well established. Various fusion methods have been advanced in literature. The current paper is based on image Fusion using PCA and Genetic Algorithm. The pictures of equal size are considered for experimentation. In order to overcome the problems of conventional techniques Genetic Algorithm can be used in collaboration with the technique of PCA (Principal Component Analysis). In Image Fusion, Genetic Algorithm can be signed when optimization of parameter is required. Also for the optimization of the weight values, Genetic algorithm is used. The various parameters used to measure the ability of image fusion technique are Mean Square Error, Entropy, Mean, Bit Error Rate, Mean, Peak Signal to Noise Ratio. From the above experiment we find that this method works well and the quality of the output image is far better than previous methods. General Terms Image fusion.

Thoracic Epidural Versus General Anaesthesia for MRM Surgeries

Background : General anaesthesia is still the preferred technique amongst many practitioners for oncologic breast surgeries. However the TEA technique has a lot of advantages over the conventional GA technique. Objective: We attempted to evaluate the two techniques of anaesthesia for MRM surgeries. Materials and Method: Sixty ASA I-II patients undergoing MRM were randomly assigned to two study groups of 30 patients each. In the TEA group (group T), an epidural catheter was inserted at T7-T8 level, and 8-10 ml of 0.5% bupivacaine was titrated and administered. GA (group G) was induced with 2mg/kg of propofol and was maintained with Isoflurane, intermittent inj. Vecuronium and 70% N2O in oxygen. The authors evaluated the adequacy of anesthesia, surgical condition, post anesthetic recovery, post anesthetic analgesia and patients' satisfaction. Results: The intra operative haemodynamics was comparable in between the two groups. The incidence of nausea and vomiting was significantly lower in the TEA group (16.5% in group T and 39.6% in group G , P = 0.02). The mean immediate VAS score was also lower in TEA group ( group T =2.4 , group G =5.8,P = 0.001). Aldrete recovery score was 9/10 in 1 st hr in a significant proportion in the TEA group (89.1% in group T v/s 59.4% in group G , P = 0.003). Patient satisfaction was significantly higher. The surgeons were however satisfied with both the methods. Conclusion: Use of thoracic epidural technique as a sole anaesthetic technique for MRM surgeries provides adequate operating conditions, better side effect profile, better pain management and patient satisfaction.

A Fast and Efficient Design Method for Circuit Analog Absorbers Consisting of Resistive Square-Loop Arrays

This paper proposes a novel optimization design method for circuit analog absorbers consisting of resistor-loaded square-loop arrays. First, new precise equivalent circuit models (ECMs) for square-loop-array-based absorbers are derived through the synthetic asymptote method. The presented models are applicable for multilayered absorber with printed single or double SLAs on each layer. Then, the combination of this ECM and conventional genetic algorithm (GA) is proposed and provided a fast and efficient way to design broadband and low-profile SLA absorbers. For demonstration, four absorbers are designed and optimized. Finally, the agreement between the simulated and measured reflection coefficients indicates the validity of the presented method.

Experimental Optimal Design of Slotless Brushless PM Machines Based on 2-D Analytical Model

This paper presents an effective optimal design procedure for brushless permanent magnet (BLPM) machines based on an adaptive metaheuristic optimization technique. 2-D analytical expressions for the calculation of the magnetic flux density, electromagnetic torque, back electromotive force, and self-inductance and mutual-inductance are employed to optimally design a slotless brushless motor with surface-mounted magnets. The proposed approach combines the computational accuracy of the 2-D analysis and the computational speed due to the analytical expressions. The objective functions are the power losses and the motor volume to be simultaneously minimized subject to three constraints: 1) the required electromagnetic torque; 2) the required maximum rotational velocity; and 3) the limit of the stator core flux density. The optimization problem is solved using a fuzzy adaptive particle swarm optimization (FAPSO) technique. To evaluate the efficacy and effectiveness of the FAPSO technique, its results are compared with those of the conventional PSO and genetic algorithms. To investigate the influence of the armature current waveforms on the design results, three different optimization problems with different armature current waveforms (ideal rectangular, six-step, and sinusoidal), but identical objectives, constraints, and optimization variables are defined and solved. Finally, a BLPM machine has been designed and manufactured to experimentally show the effectiveness of the proposed technique.

A hybrid co-evolutionary genetic algorithm for multiple nanoparticle assembly task path planning

In this paper, a hybrid co-evolutionary genetic algorithm (HCGA) has been presented for determining the optimal moving paths of several nanoparticles in a complex environment. In the proposed approach, an artificial potential field (APF) has been used to determine the feasible initial paths for moving the nanoparticles. The proposed APF prepares a potential map of the environment using the initial positions of the nanoparticles and positions of the obstacles and surface roughness. The cost function used in this paper includes the area under the critical force-time diagram, surface roughness as well as path smoothness. The performed investigations indicate the importance of each of these parameters in determining the optimal paths for displacing the nanoparticles. Also, the dynamic application of crossover and mutation operators has been used to avoid premature convergence. Using the information of the potential map, two new operators have been introduced to improve the feasible and infeasible paths. Furthermore, a novel co-evolutionary mechanism for solving the multiple nanoparticle path planning problems has been presented. The proposed co-evolutionary mechanism is able to determine the best destination for each particle, optimal sequence of moves for several particles, and also the optimal path for moving each particle. Finally, the performance of the proposed HCGA has been compared with the conventional genetic algorithm (CGA) and the ant colony optimization algorithm.

Designing a Neuro-Sliding Mode Controller for Networked Control Systems with Packet Dropout

This paper addresses control design in networked control system by considering stochastic packet dropouts in the forward path of the control loop. The packet dropouts are modelled by mutually independent stochastic variables satisfying Bernoulli binary distribution. A sliding mode controller is utilized to overcome the adverse influences of stochastic packet dropouts in networked control systems. Firstly, to determine the parameters of switching function used in the sliding mode control design, an improved genetic algorithm is applied. The proposed improved genetic algorithm provides a fast convergence rate and a proper dynamic performance in comparison with conventional genetic algorithms especially in online control applications. Then, an adaptive neural sliding mode control based on radial-basis function neural network approximation is proposed to eliminate chattering phenomenon in the sliding mode control. A numerical example is given to illustrate the effectiveness of the proposed controller in networked control systems. The results show that the proposed controller provides high-performance dynamic characteristics and robustness against plant parameter variations and external disturbances.

Optimal controllers designs for automatic reactive power control in an isolated wind-diesel hybrid power system

The paper presents the bacterial foraging optimization algorithm (BFOA) and particle swarm optimization (PSO) algorithm based robust controllers for voltage deviations due to the variation of reactive power in an isolated wind-diesel hybrid power system. The isolated wind-diesel system consists of wind energy conversion system (WECS) utilizing a permanent magnet induction generator (PMIG). Further, a synchronous generator (SG) is used with the diesel engine set for power generation. The mismatch between generated and consumed reactive power in the system causes voltage fluctuations, which will occur at generator terminals. These oscillations further causes reduction in the stability and quality of the power supply. The static synchronous compensator (STATCOM) and an automatic voltage regulator (AVR) are used to suppress voltage fluctuations in an isolated wind-diesel hybrid power system. The STATCOM is used as a reactive power compensator and the AVR is used to keep the terminal voltage constant for the synchronous generator. Both STATCOM and AVR are having proportional and integral (PI) controllers with single input. In modeling for the system, a normalized co-prime factorization is applied to show the possible unstructured uncertainties in the power system such as variation of system parameters and generating and loading conditions. The performance and robust stability conditions of the control system are formulated as the optimization problem, which is based on the Hα loop shaping. BFOA and PSO algorithms are implemented to solve this optimization problem and to achieve PI control parameters of STATCOM and AVR simultaneously. In order to show the efficiency of the proposed controllers, the performance of the proposed controllers is compared with the performance of the conventional controller and genetic algorithm (GA) based PI controllers for the same wind-diesel system. The dynamic responses of the system for four different small-disturbance case studies has been carried out in MATLAB environment.

Fitness switching genetic algorithm for solving combinatorial optimization problems with rare feasible solutions

The goal of meta-heuristic algorithms such as genetic algorithm is to explore the search space of the combinatorial optimization problems efficiently to locate the optimal solutions, the feasible solutions with the best output values. However, typical meta-heuristic algorithms implicitly assume that the feasible solutions for the given problems can be generated easily, and they can fail to solve the problems with rare feasible solutions in effective manner. In this context, this paper aims to introduce the maze-type shortest path problem as an example of the combinatorial optimization problem with rare feasible solutions and to propose the fitness switching genetic algorithm for solving it. The maze-type shortest path problem is characterized by the maze-type network that contains many dead-ends, and the conventional genetic algorithms based on the population of feasible paths are not appropriate for finding the optimal path in such networks. On the contrary, this paper introduces the fitness switching and fitness leveling operations for maintaining the population of both feasible and infeasible paths during the search procedure. In addition, the infeasible paths are randomly modified by the simple local search of the proposed algorithm to find the feasible paths more quickly. The experiment results show that the proposed algorithm can address the issues in the combinatorial optimization problems with rare feasible solutions very effectively.

Micromachined multiple focused-ion-beam devices

Micromachined multiple focused-ion-beam devices were developed, which were composed of an array of ion sources, electrostatic extractors, and lenses to achieve concurrent processing of microdevices with high throughput. All of the parts of the devices were fabricated and packaged on the scale of a microchip, employing Si bulk micromachining technologies. Ionic liquids (ILs) were used to generate the ion species instead of conventional liquid metals such as Ga and In. Three types of testing devices were fabricated to comprehensively evaluate the system. The first, device 1, consisted of the emitter arrays and a biased Si target, whereby the etching characteristics of beams generated with several ILs were evaluated. The peaks of SiF+, SiF2+, SiF3+, and SiF4+, which provide evidence for reactive etching of Si, were obtained by in situ monitoring of the gas composition. Compared with a conventional Ga ion source, the ion beams emitted from the IL 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide exhibited a 5.4-fold sputtering yield of 11.9 atom/ion at an acceleration voltage of 5 kV. In device 2, the electrostatic extractor and lens were mounted on the emitter array, and control of the divergence angle of the ion beam was confirmed. In device 3, which has a separated extractor on the electrostatic lens plate, an individual control of ion beams was demonstrated.

Experimental study of seeding in genetic algorithms with non-binary genetic representation

Seeding is a technique used to leverage population diversity in genetic algorithms. This paper presents a quick survey of different seeding approaches, and evaluates one of the promising ones called the Seeding Genetic Algorithm. The Seeding GA does not include mutation, and it has been shown to work well on some GA-hard problems with binary representation, such as the Hierarchical If-and-Only-If or Deceptive Trap. This paper investigates the effectiveness of the Seeding GA on two problems with more complex non-binary representations: capacitated lot-sizing and single-machine scheduling. The results show, with statistical significance, that the new GA is consistently outperformed by the conventional GA, and that not including mutation is the main reason why. A detailed analysis of the results is presented and suggestions are made to enhance and improve the method.

Taming the 0/1 knapsack problem with monogamous pairs genetic algorithm

This paper defines and explores a somewhat different subclass of genetic algorithm (GA) – a monogamous pairs genetic algorithm (MopGA) for solving the 0/1 knapsack problems (0/1-KP). The MopGA incorporates two important operations borrowed from social monogamy: pair bonding and infidelity at a low probability. Unlike conventional GAs, same pairs of parents (monogamous parents) are re-mated at each generation until their bonds expire. Nonetheless, this monogamy rule may be violated at the presence of infidelity. Our technique emphasizes on the thorough exploitation of the current search region via pair bonding, while allowing sufficient exploration to other unknown regions via infidelity. Consequently, MopGA is able to preserve higher population diversity by shielding offspring under the monogamous parents from population-wide selection pressure and restrictive mating strategy. As a side benefit from economical use of selection mechanism, the MopGA is computationally more efficient, especially when dealing with high-dimensionality 0/1-KPs. The empirical results on 0/1-KPs also show considerable performance in favour of the proposed methodology in terms of solution quality.

Swarm-Based Nature-Inspired Algorithm and Genetic Algorithms for Optimizing a Sun Tracker Trajectory

ABSTRACTOver the last 10 years, the popularity of solar panels for catching solar energy has reduced development and manufacturing costs. Nevertheless, costs per watt are still high when compared to other less-clean energy sources such as wind energy. Therefore, the goal of the sun tracker is to maximize the energy generation of solar cells, thus giving a competitive advantage to solar energy. However, finding the optimal position is a very complex task and different algorithms such as genetic algorithms or swarm-based optimization algorithms have been used to improve the results. This article shows the design and implementation of two optimal sun tracker algorithms. The first method presented is genetic algorithms, which allow finding the position of the sun tracker based on an offline solution. When genetic algorithms find the solution offline, the results can be programmed in a simple lookup table. This approximation decreases the computational cost, and it is effective for geographical climes where conditions are constant. However, there are places with nonconstant climate conditions that need online optization algorithms. In this case, a newly developed intelligent water drop algorithm is proposed for running an online solution. Both methods were designed for the sun tracker problem and were implemented. The power and energy analytics show that the algorithms increase the efficiency of the sun tracker, compared to a static solar cell, by at least 40% in some cases. The sun tracker presented gives an excellent solution for obtaining energy from the sun during diverse weather conditions. This work also introduces a novel derivation of the intelligent water drop algorithm for sun trackers based on a nonconventional trajectory and conventional genetic algorithms adjusted for sun tracker needs. The experimental results are shown in order to validate the methodologies proposed.

An optimised genetic algorithm for energy aware grid computing with limited iterations

Grid computing is one of the emerging computing platforms that handles both parallel and distributed computing. This type of the grid environment appends the complicated nature to the scheduler. Genetic algorithm (GA) is a generally used approach by researchers to figure out this type of NP-complete problems. Yet, the conventional GA is also sluggish to figure out the scheduling issues in the realistic environment due to its time consuming iterations. In this composition, we adopt the independent batch scheduling by considering the objective as energy expenditure as the scheduling criteria. Here, we proposed an optimised energy aware genetic algorithm (OGA), which is suitable for grid scheduling, and it can improve the search performance by limited iterations and increase the computing capability of finding the reasonable solution.

Dynamic Path Planning Algorithm for a Mobile Robot Based on Visible Space and an Improved Genetic Algorithm

In this study, a series of new concepts and improved genetic operators of a genetic algorithm (GA) was proposed and applied to solve mobile robot (MR) path planning problems in dynamic environments. The proposed method has two superiorities: fast convergence towards the global optimum and the feasibility of all solutions in the population. Path planning aims to provide an optimal path from a starting location to a target location, preventing collision or so-called obstacle avoidance. Although GAs have been widely used in optimization problems and can obtain good results, conventional GAs have some weaknesses in an obstacle environment, such as infeasible paths. The main ideas in this paper are visible space, matrix coding and new mutation operators. In order to demonstrate the superiority of this method, three different obstacle environments have been used and an experiment is conducted. This algorithm is effective in both static and dynamic environments.

Reporting cell planning for cost reduction using binary genetic algorithm

In mobile access networks, location management is a major and complex issue where location information of the mobile user is essential to deliver incoming calls/SMS to the appropriate mobile stations (MSs). Many metaheuristic algorithms are developed to solve such types of important issue which is responsible for determining the network configuration. This paper proposes a conventional binary genetic algorithm (BGA) approach implemented to reporting cells planning problem in complex multimodal search spaces, since BGA is a conventional algorithm sufficiently able to solve the signalling cost problem along with the modifications in the generic operators. These modifications occur in the mutation operator. The simulation results obtained from the experiments conducted using the existing database along with the real time database. The proposed method shows better performance compared to existing algorithm. This simulation results obtained from the experiments reflects the effectiveness of the conventional BGA.