A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding

The multi-level thresholding is a popular method for image segmentation. However, the method is computationally expensive and suffers from premature convergence when level increases. To solve the two problems, this paper presents an advanced version of gravitational search algorithm (GSA), namely hybrid algorithm of GSA with genetic algorithm (GA) (GSA-GA) for multi-level thresholding. In GSA-GA, when premature convergence occurred, the roulette selection and discrete mutation operators of GA are introduced to diversify the population and escape from premature convergence. The introduction of these operators therefore promotes GSA-GA to perform faster and more accurate multi-level image thresholding. In this paper, two common criteria (1) entropy and (2) between-class variance were utilized as fitness functions. Experiments have been performed on six test images using various numbers of thresholds. The experimental results were compared with standard GSA and three state-of-art GSA variants. Comparison results showed that the GSA-GA produced superior or comparative segmentation accuracy in both entropy and between-class variance criteria. Moreover, the statistical significance test demonstrated that GSA-GA significantly reduce the computational complexity for all of the tested images.     

A new local search based hybrid genetic algorithm for feature selection

This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS), called as HGAFS. The vital aspect of this algorithm is the selection of salient feature subset within a reduced size. HGAFS incorporates a new local search operation that is devised and embedded in HGA to fine-tune the search in FS process. The local search technique works on basis of the distinct and informative nature of input features that is computed by their correlation information. The aim is to guide the search process so that the newly generated offsprings can be adjusted by the less correlated (distinct) features consisting of general and special characteristics of a given dataset. Thus, the proposed HGAFS receives the reduced redundancy of information among the selected features. On the other hand, HGAFS emphasizes on selecting a subset of salient features with reduced number using a subset size determination scheme. We have tested our HGAFS on 11 real-world classification datasets having dimensions varying from 8 to 7129. The performances of HGAFS have been compared with the results of other existing ten well-known FS algorithms. It is found that, HGAFS produces consistently better performances on selecting the subsets of salient features with resulting better classification accuracies.     

A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem

The genetic algorithm with search area adaptation (GSA) has a capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local searches, even if we do not adjust the parameters of the genetic algorithm (GA), such as crossover and/or mutation rates. But, GSA needs the crossover operator that has ability for characteristic inheritance ratio control. In this paper, we propose the modified genetic algorithm with search area adaptation (mGSA) for solving the Job-shop scheduling problem (JSP). Unlike GSA, our proposed method does not need such a crossover operator. To show the effectiveness of the proposed method, we conduct numerical experiments by using two benchmark problems. It is shown that this method has better performance than existing GAs.     

基于Web页面平均质量的Web搜索模型和优化算法

针对Web搜索引擎的特点,提出了一种基于量子遗传克隆挖掘(QGCMA)的搜索策略。该算法将用户的查询描述为Web页面的平均质量,并通过克隆,变异,交叉的操作获取具有高亲和度的抗体（Web页面）。通过实验结果分析得出,在Web搜索中该方法比标准的遗传算法(GA)具有较明显的优势。     

A genetic algorithm based lookup table approach for optimal stepping sequence of open-loop stepper motor systems

Stepper motor driven systems are widely used in industrial applications. They are mainly used for their low cost open-loop high performance. However, as dynamic systems need to be increasingly faster and their motion more precise, it is important to have an open-loop system which is accurate and reliable. In this paper, we present a novel technique in which a genetic algorithm (GA) based lookup table approach is used to find the optimal stepping sequence of an open-loop stepper motor system. The optimal sequence objective is to minimize residual vibration and to accurately follow trajectory. A genetic algorithm is used to find the best stepping sequence which minimizes the error and improves the system performance. Numerical simulation has showed the effectiveness of our approach to improve the system performance for both position and velocity. The optimized system reduced the residual vibration and was able to follow the trajectory with minimal error.     

An integrated genetic algorithm complemented with direct search for optimum design of RC frames

This paper presents an improved optimum design method for reinforced concrete (RC) frames using an integrated genetic algorithm (GA) with a direct search method. A conventional genetic algorithm occasionally has limitations due to a low convergence rate in spite of high computing times. The proposed method in this research uses a predetermined section database (DB) when determining trial sections for the next iteration.From an initial section determined by substituting calculated member forces into a regression formula, a direct search that determines a final discrete solution is followed within a limited range in the section database. Due to the fast convergence and the sequential determination of feasible trial sections close to the final optimum solution, an introduction of the search procedure at each iteration allows difficulties to be solved during the application of a conventional GA to large RC structures.Finally, the effectiveness of the introduced design procedure is verified through correlative tests of the introduced design procedure.     

A comprehensive conformational space analysis of N-formyl-l-tryptophanamide system by using a genetic algorithm for multi-modal search

The conformational space of protected amino acid HCO-Tryptophane-NH₂ was explored by using a new optimization procedure, in order to localize the stable minima on its potential energy surface (PES). The genetic algorithm based on the Multi-Niche Crowding (MNC) technique was used initially to generate a set of optimized structures for title compound. Resulting structures from the genetic algorithm technique will be used hereafter as input conformers at a hierarchy of increasingly more accurate electronic structure calculations (RHF/6-31G+(d) and DFT/B3LYP/6-31G+(d) geometry optimizations). The lowest energy conformer γ_L(g+g+) presents a folded Backbone that is stabilized by strong hydrogen bond noted C₇. This links the carbonyl oxygen of the formyl group and the hydrogen of the amine group. There are further interactions from one hand between the carbonyl oxygen of the formyl group and the neighboring CH group on the pyrrole ring and from other hand between the N-terminus hydrogen and the indole ring in accordance with the experimental results. This work includes also a comparison between the theoretical calculations and the experimental results of X-ray crystallography extracted from protein data bank (PDB).     

A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP)

Wireless sensor networks have become increasingly popular because of their ability to cater to multifaceted applications without much human intervention. However, because of their distributed deployment, these networks face certain challenges, namely, network coverage, continuous connectivity and bandwidth utilization. All of these correlated issues impact the network performance because they define the energy consumption model of the network and have therefore become a crucial subject of study. Well-managed energy usage of nodes can lead to an extended network lifetime. One way to achieve this is through clustering. Clustering of nodes minimizes the amount of data transmission, routing delay and redundant data in the network, thereby conserving network energy. In addition to these advantages, clustering also makes the network scalable for real world applications. However, clustering algorithms require careful planning and design so that balanced and uniformly distributed clusters are created in a way that the network lifetime is enhanced. In this work, we extend our previous algorithm, titled the zone-based energy efficient routing protocol for mobile sensor networks (ZEEP). The algorithm we propose optimizes the clustering and cluster head selection of ZEEP by using a genetic fuzzy system. The two-step clustering process of our algorithm uses a fuzzy inference system in the first step to select optimal nodes that can be a cluster head based on parameters such as energy, distance, density and mobility. In the second step, we use a genetic algorithm to make a final choice of cluster heads from the nominated candidates proposed by the fuzzy system so that the optimal solution generated is a uniformly distributed balanced set of clusters that aim at an enhanced network lifetime. We also study the impact and dominance of mobility with regard to the variables. However, before we arrived at a GFS-based solution, we also studied fuzzy-based clustering using different membership functions, and we present our understanding on the same. Simulations were carried out in MATLAB and ns2. The results obtained are compared with ZEEP.     

A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition

This paper presents a novel adaptive cuckoo search (ACS) algorithm for optimization. The step size is made adaptive from the knowledge of its fitness function value and its current position in the search space. The other important feature of the ACS algorithm is its speed, which is faster than the CS algorithm. Here, an attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step. The proposed algorithm is validated using twenty three standard benchmark test functions. The second part of the paper proposes an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA). The proposed algorithms are named as PCA + IDA and ACS–IDA. Interestingly, PCA + IDA offers us a perturbation free algorithm for dimension reduction while ACS + IDA is used to find the optimal feature vectors for classification of the face images based on the IDA. For the performance analysis, we use three standard face databases—YALE, ORL, and FERET. A comparison of the proposed method with the state-of-the-art methods reveals the effectiveness of our algorithm.     

A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams

A hybrid approach based on an improved gravitational search algorithm (IGSA) and orthogonal crossover (OC) is proposed to efficiently find the optimal shape of concrete gravity dams. The proposed hybrid approach is called IGSA-OC. The hybrid of IGSA and the OC operator can improve the global exploration ability of the IGSA method, and increase its convergence rate. To find the optimal shape of concrete gravity dams, the interaction effects of dam–water–foundation rock subjected to earthquake loading are considered in this study. The computational cost of the optimal shape of concrete gravity dams subjected earthquake loads is usually high. Due to this problem, the weighted least squares support vector machine (WLS-SVM) regression as an efficient metamodel is utilized to considerably predict dynamic responses of gravity dams by spending low computational cost. To testify the robustness and efficiency of the proposed IGSA-OC, first, four well-known benchmark functions in literatures are optimized using the proposed IGSA-OC, and provides comparisons with the standard gravitational search algorithm (GSA) and the other modified GSA methods. Then, the optimal shape of concrete gravity dams is found using IGSA-OC. The solutions obtained by the IGSA-OC are compared with those of the standard GSA, IGSA and particle swarm optimization (PSO). The numerical results demonstrate that the proposed IGSA-OC significantly outperforms the standard GSA, IGSA and PSO.     

Harmony search based algorithm for the optimum design of grillage systems to LRFD-AISC

Harmony search based optimum design method is presented for the grillage systems. This numerical technique imitates the musical performance process that takes place when a musician searches for a better state of harmony. Jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The design algorithm considers the serviceability and ultimate strength constraints which are implemented from Load and Resistance Factor Design—American Institute of Steel Construction (LRFD-AISC). It selects the appropriate W-sections for the transverse and longitudinal beams of the grillage system out of 272 discrete W-section designations given in LRFD-AISC. This selection is carried out such that the design limitations described in LRFD-AISC are satisfied and the weight of the system is the minimum. Many design examples are considered to demonstrate the efficiency of the algorithm presented.     

Design of efficient packing system using genetic algorithm based on hyper heuristic approach

In practice the maximum usage of container space arises in many applications which is one of the crucial economical requirements that have a wide impact on good transportation. A huge amount of monetary infrastructure is spent by companies on packing and transportation. This study recommends that there exists a scope for further optimization which if implemented can lead to huge saving. In this paper, we propose a new hyper heuristic approach which automates the design process for packing of two dimensional rectangular blocks. The paper contributes to the literature by introducing a new search technique where genetic algorithm is coupled with the hyper heuristic to get the optimal or sub optimal solution at an acceptable rate. The results obtained show the benefits of hyper-heuristic over traditional one when compared statistically on large benchmark dataset at the 5% level of significance. Improvements on the solution quality with high filling rate up to 99% are observed on benchmark instances.     

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.     

A novel methodology for stock investment using high utility episode mining and genetic algorithm

In this paper, we present a novel methodology for stock investment using the technique of high utility episode mining and genetic algorithms. Our objective is to devise a profitable episode-based investment model to reveal hidden events that are associated with high utility in the stock market. The time series data of stock price and the derived technical indicators, including moving average, moving average convergence and divergence, random index and bias index, are used for the construction of episode events. We then employ the genetic algorithm for the simultaneous optimization on parameters and selection of subsets of models. The empirical results show that our proposed method significantly outperforms the state-of-the-art methods in terms of annualized returns of investment and precision. We also provide a set of Z-tests to statistically validate the effectiveness of our proposed method. Based upon the promising results obtained, we expect this novel methodology can advance the research in data mining for computational finance and provide an alternative to stock investment in practice.     

A new hybrid algorithm for university course timetabling problem using events based on groupings of students

In this paper, a new hybrid algorithm (NHA) combining genetic algorithm with local search and using events based on groupings of students is described to solve the university course timetabling problem. A list of events such as lectures, tutorials, laboratories and seminars are ordered and mutually disjoint groups of students taking them are formed in such a way that once a student is selected in any group, he is excluded from further selection in other groups. The union of all the events taken by all the students of each group is formed. The number of events in each group is termed as its group size whose upper bound is restricted by the total number of timeslots and can be reduced to the maximum number of events per student. The above process of forming groups is repeated till the size of each group is reduced within this bound by not choosing those events which are common for all the students in the group. Now, the genetic algorithm with local search (GALS) is applied on a number of benchmark problems. The experimental results show that our algorithm, NHA, is able to produce promising results when compared with the results obtained by using GALS and other existing algorithms.     

A hybrid simulation and genetic algorithm approach to determine the optimal scheduling templates for open access clinics admitting walk-in patients

With the pressing demand for improving patient accessibility, the traditional scheduling system may not be effective for mitigating the adverse effects caused by no-shows, appointment cancellations and late arrivals. For this reason, open access scheduling, which specifies that a portion of clinic appointment slots be reserved for short-notice appointments, was proposed and adopted in recent years. In literature, many studies have developed a variety of approaches and models to optimize the open access scheduling systems, while few considers the inclusion of walk-in patients and the optimal allocation of reserved slots on the scheduling template under the open access configuration. In this paper, we propose a Discrete Event Simulation and Genetic Algorithm (DES–GA) approach to find the heuristic optimal scheduling template under the clinic setting that allows both open access and walk-in patients. The solution can provide scheduling templates consisting of not only the optimal number of reservations for open access appointments and walk-ins, but also the optimized allocation of these reserved slots, by minimizing the average cost per admission of open access or walk-in patient. In this approach, the cost is measured by the weighted summation of patient waiting time, provider idle time, and provider overtime. A case study and sensitivity analysis are conducted to show how the heuristic optimal scheduling template generated from the proposed approach could vary under different scenarios. This also illustrates the viability of our model. The results show that the heuristic optimal scheduling templates are significantly affected by the patient attendance rate, level of demands of same-day appointment and walk-in admissions, as well as the cost coefficients associated with patient waiting time, provider idle time and provider overtime.     

A hybrid approach based on stochastic competitive Hopfield neural network and efficient genetic algorithm for frequency assignment problem

This paper presents a hybrid efficient genetic algorithm (EGA) for the stochastic competitive Hopfield (SCH) neural network, which is named SCH–EGA. This approach aims to tackle the frequency assignment problem (FAP). The objective of the FAP in satellite communication system is to minimize the co-channel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate increasing demands. Our hybrid algorithm involves a stochastic competitive Hopfield neural network (SCHNN) which manages the problem constraints, when a genetic algorithm searches for high quality solutions with the minimum possible cost. Our hybrid algorithm, reflecting a special type of algorithm hybrid thought, owns good adaptability which cannot only deal with the FAP, but also cope with other problems including the clustering, classification, and the maximum clique problem, etc. In this paper, we first propose five optimal strategies to build an efficient genetic algorithm. Then we explore three hybridizations between SCHNN and EGA to discover the best hybrid algorithm. We believe that the comparison can also be helpful for hybridizations between neural networks and other evolutionary algorithms such as the particle swarm optimization algorithm, the artificial bee colony algorithm, etc. In the experiments, our hybrid algorithm obtains better or comparable performance than other algorithms on 5 benchmark problems and 12 large problems randomly generated. Finally, we show that our hybrid algorithm can obtain good results with a small size population.     

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

In this paper, a hybrid gravitational search algorithm (GSA) and pattern search (PS) technique is proposed for load frequency control (LFC) of multi-area power system. Initially, various conventional error criterions are considered, the PI controller parameters for a two-area power system are optimized employing GSA and the effect of objective function on system performance is analyzed. Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation. After that PS is employed to fine tune the best solution provided by GSA. Further, modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS (hGSA-PS) approach. The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm (FA), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), particle swarm optimization (PSO), hybrid BFOA-PSO, NSGA-II and genetic algorithm (GA) for the same interconnected power system. Additionally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. Also, the proposed approach is extended to two-area reheat thermal power system by considering the physical constraints such as reheat turbine, generation rate constraint (GRC) and governor dead band (GDB) nonlinearity. Finally, to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients, the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA-PS technique.     

A novel classification method: A hybrid approach based on extension of the UTADIS with polynomial and PSO-GA algorithm

In this study, a new multi-criteria classification technique for nominal and ordinal groups is developed by expanding the UTilites Additives DIScriminantes (UTADIS) method with a polynomial of degree T which is used as the utility function rather than using a piecewise linear function as an approximation of the utility function of each attribute. We called this method as PUTADIS. The objective is calculating the coefficients of the polynomial and the threshold limit of classes and weight of attributes such that it minimizes the number of misclassification error. Estimation of unknown parameters of the problem is calculated by using a hybrid algorithm which is a combination of particle swarm optimization algorithm (PSO) and Genetic Algorithm (GA). The results obtained by implementing the model on different datasets and comparing its performance with other previous methods show the high efficiency of the proposed method.