Abstract: In view of the single curved deformation characteristics of zero Poisson’s ratio (ZPR) honeycomb, Arrow honeycomb with ZPR is applied to the reflecting surface of the spatial parabolic cylinder antenna in this paper. First of all, the loading method for cylinder formation of honeycomb with ZPR is analysed. Then, Abaqus analysis, surface fitting and precision calculation are integrated into the objective function. AntlionOptimization (ALO) algorithm is used to optimize the RMS error of the deformed surface. The analysis results show that it is feasible to apply the honeycomb with ZPR to the reflector of cylindrical antenna, and the accuracy of deformed profile is improved by 45.5% through the ALO algorithm, which proves the effectiveness and efficiency of the ALO algorithm to solve the problem with unknown initial value. The research results are certainly significant for the optimization design of honeycomb with ZPR and the application of ALO algorithm.
Abstract. Swarm intelligence based image enhancement method is an important method to improve the image visual effect. In order to improve the image enhancement algorithm based on the antlionoptimization, a quadratic interpolation technique is introduced to improve the image enhancement algorithm of the antlionoptimization method which has the disadvantages of slow convergence speed and low computational accuracy. The new algorithm uses quadratic interpolation to accelerate the convergence speed of ant population and antlion population, to balance the global and local searching ability of the antlion algorithm, so as to improve the estimation precision of the optimal parameters in the incomplete Beta function and improve the image quality. The two evaluation indexes verify that the new algorithm is better than the contrast algorithm in statistical meaning, and extend the research of the antlionoptimization algorithm.
Abstract. Because of slow convergence speed and low calculation precision of antlionoptimization (ALO) algorithm, an improved algorithm named quadratic interpolation antlionoptimization (QIALO) is put forward in this paper puts. The new algorithm uses the quadratic interpolation (QI) to obtain the secondary renewal position of ants, which enhances the local search ability of the antlionoptimization algorithm and accelerates the global optimization speed of the population. Simulation results on thirteen test functions indicate that the performance of the new algorithm is better than that of the contrast algorithms in the statistical sense, and the performance of QIALO algorithm for multimodal optimization is improved effectively.
The AntlionOptimization algorithm is a novel optimization algorithm which mimics the hunting process of antlions in nature. It was first proposed by Australian scholar Mirjalili in 2015 . An antlion larvae digs a cone-shaped pit by rotating in the sand as it drills down -. It hides under the bottom of the funnel and waits for insects to be trapped . When ants or other insects climb into traps, they slide down because of loose sands, antlion will cast sands out continuously. Then antlions with large jaws will clamp their prey and eat it under the sand. Then the corpse will be thrown out of the trap for the next hunting .
Basic constraints that will dependent with excellence of developed circuit existed with knowledge belonging to the engineering along with duration and exertion incurred with formulation.The complete area belonging to formulation will be seldom investigated, transistors operating with prominently frail along withreasonable functioning areas, that might contain maximum suitability for power conscious designs . For Involuntary combining belonging to formulation area pertaining to analog integrated circuits made up of CMOS transistors will extremely unproportioned.Lot of loosely bounded variables involved in the formulation of characteristic integrated circuits that might be function in analogmode like two phase operational amplifier circuits that might be varying with sizes, currents or inversion level.During certain instances the association among stipulation developed with formulation of circuit along with dimension will be contradictory. Hence, huge quantity of issues might be developed in determining the best solutions. Involuntary formulation of basic stages involved in the design of integrated circuits that functions with analog mode will be obligator for making the circuit into the successful one.Selection of appropriate dimension to device will be the toughest task along with more amount of time consumed developing latency in manufacturing in automated formulation technique because of huge along with extremely unproportioned area.Currently devoid of availability of strategies that utilizes the computer area of formulation was observed. Reduction in the formulation duration might be resulted with the utilization of soft computing techniques. Functioning with global investigation which simulates certain naturally inspired performed was observed for determining the best values for characteristics for the projected challenge.For determining the solutions to issues related with formulation of ICs varying from sectioned proper dimension to fanout was performed with solitary motivational strategies with specified constraints. Degerming the best solutions with diverse motivational operations for concurrent accomplishment of best values for all the parameters specified is performed by means of multi-objective optimization approach. Mentioned approach might be established in diverse stages such while performing ___________________________
Abstract—The suppression of the side-lobe level (SLL) of antenna arrays is a signiﬁcant factor that can enhance the reliability and validity of a communication system. Recently, metaheuristic algorithms have been widely implemented in the design of antenna arrays, in order to ﬁnd the optimal minimization for the side-lobe level of the array’s radiation pattern. In this paper, we propose a new hybrid algorithm that combines the characteristics of two stochastic algorithms, AntlionOptimization (ALO) algorithm and Grasshopper Optimization Algorithm (GOA). ALO, which is an evolutionary algorithm, is robust in exploitation and has been eﬀectively used in many articles in the literature. GOA has strong capability of exploration all over the search space due to the swarm nature of the algorithm, which has been proven in several articles in the literature. Therefore, combining these characteristics and overcoming the drawbacks of ALO and GOA are the main motivation behind hybridizing ALO and GOA in one hybrid algorithm. Simulation results show that the proposed hybrid algorithm has a good performance in the radiation pattern optimization of circular antenna array (CAA) and fast convergence rate compared with other strong optimization algorithms, which prove the eﬃciency, robustness, and stability of the hybrid algorithm.
As definition shows , all Pareto optimal solutions composed set is called Pareto optimal solution set, multi-objective problem solution’s primary link is looking for a Pareto optimal solution set in decision space, in the solution set, to every Pareto optimal solution, a objective-oriented improvement is at the cost of reducing another objective functions, so let multiple objectives to simultaneously arrive at optimal value is impossible, and it can only make coordination and tradeoff among them, let each objective function to try to arrive at approximately optimal as much as possible. Hu Yu-Da (1990)pointed out except for some special multi-objective programming , most of multi-objective scale cannot be solved directly, generally adopted method is converting multi-objective optimization problem into single objective optimization problem that its optimization methods are relative mature, from which more used methods are weighted method and constraint method, there are lots of methods can be uses in the aspect, such as voting analytic hierarchy process ､ average weighting method､ ideal point method､virtual target method and other mathematical programming methods .
SRGMs are the statistical models which can be used to make predictions about a Software System's Failure Rate, given the failure history of the system. As the failure intensity decreases, the reliability increases. Meta-heuristic Algorithms are those which are based on the natural behavior of the some special kind of animals. They have tremendous ability to solve various discrete optimization problems. We can use various software reliability models for evaluating and predicting the activities and presentation of the software reliability .Some Approaches such as MLE or LSE which were classical in nature, were applied for parameter estimation of SRGM but were not useful in solving non-linear optimization problems. Due to these obstacles, Meta-Heuristic Algorithms are introduced by some researchers.
As one of open-source codes widely used in computational ocean acoustics, FOR3D can provide a very good estimate for underwater acoustic propagation. In this paper, we propose a performance optimization and parallelization to speed up the running of FOR3D. We utilized a variety of methods to enhance the entire performance, such as using a multi-threaded programming model to exploit the potential capability of the many-core node of high-performance computing (HPC) system, tuning compile options, using efficient tuned mathematical library and utilizing vectorization optimization instruction. In addition, we extended the application from single-frequency calculation to multi-frequency calculation successfully by using OpenMP+MPI hybrid programming techniques on the mainstream HPC platform. A detailed performance evaluation was performed and the results showed that the proposed parallelization obtained good accelerated effect of 25.77 × when testing a typical three-dimensional medium-sized case on Tianhe-2 supercomputer. It also showed that the tuned parallel version has a weak-scalability. The speed of calculation of underwater sound field can be greatly improved by the strategy mentioned in this paper. The method used in this paper is not only applicable to other similar computing models in computational ocean acoustics but also a guideline of performance enhancement for scientific and engineering application running on modern many-core-computing platform.
optimization algorithms can be used to assist deep learning methods in order to get better finding results. Several optimization methods such as particle swarm optimization, enhanced binary particle swarm optimization, genetic algorithm and differential search algorithm have been reviewed as well as their involvement in bioinformatics field. Just like Table 1, Table 2 also has summarized all advantages and disadvantages occur in optimization algorithms and have been visualize in a table form for easy and better understanding.
In order to show how to solve the economic load dispatch problem by using grey wolf optimization algorithm and to verify the feasibility of GWO, two practical power systems are engaged to test the algorithm. In practical power systems, the ramp rate limit and prohibited operating zones are included. The proposed GWO algorithm results are compared with PSO-ANFIS technique by two power systems of six unit and fifteen unit test systems.
Abstract— This paper represents the efficiency of the Ant inspired Bacterial Foraging Optimization (ABFO), the hybrid technique of Ant Colony Optimization (ACO) algorithm and Bacterial Foraging Optimization (BFO) algorithm from Bio Inspired Computing to solve the Open Shop Scheduling Problems (OSSP). The Ant inspired Bacterial Foraging Optimization (ABFO) was tested on the Open Shop Scheduling benchmark problems from the literature. The computational method of ABFO algorithm has shown improvement in finding the optimal solutions when compared with BFO algorithm for all small level test problems. Although ABFO algorithm has not achieved the best known solutions for larger instances of benchmarks problems, the optimal values are highly comparable to other best performing algorithms.
The process of building energy management based on EUSBS is briefly illustrated as follows. First, in the day-ahead stage, the electricity use data of controllable equipment in the building is uploaded to the cloud server via the EUSBS based on artificial establishment. In addition, users’ requirements for comfort can also be uploaded to the cloud server via the EUSBS. Then, according to the uploaded data of electricity use, the server carries out building energy calculations via data mining and cloud computing, in order to optimize the day-head output scheduling of equipment, such that the output scheduling instructions of all categories of equipment can be obtained. Thereby these output scheduling instructions are sent to the EUSBS for storage. Finally, at the time of the day, the energy USB concentrator will give these control instructions to equipment at the appropriate time to achieve coordination and control of the equipment, so as to reduce the operation costs of the building. In this process, the ultimate goal of comprehensive energy dispatching and optimization for the building is to achieve the minimum total operation cost in an operational cycle. In this section, we first establish the building equipment model, including a combined cooling heating and power (CHP) model, a fuel cell model, an electric boiler model, a storage battery model, and room temperature control system model. Then, we determine the objective function, namely minimizing the total operation cost, as depicted earlier. In addition, the relevant constrained conditions should be taken into account, including electric power equilibrium constraint, heat power balance constraint, power interaction constraint, controllable units’ constraint, and storage battery constraint. Based on the established model, objective function and constrained conditions, a comprehensive commercial building is selected as the control and optimization object to carry out a case study.
Yanfang Deng and Hengqing Tong (2010) proposed a PSO particle swarm optimization . In there work, a hybrid PSO algorithm combined fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO algorithm. Simulation experiments have been carried out on different traffic network topologies consisting of 15–70 nodes and the results showed that the proposed approach can find the optimal path with good success rates and also can find closer sub-optimal paths with high success ratio for all the tested traffic networks. The shortest path algorithm is critical for dynamic traffic assignment and for the realization of route guidance in intelligent transportation systems (ITS) .
probabilities. We consider short (1 quarter), medium (5 quarters) and long (20 quarters) fore- cast horizons. For the purpose of this exercise, following SW we lump together government spending, investment and risk preference shocks into a unified demand shock. Additionally we lump together the price markup and wage markup shocks into a unified markup shock. A few interesting results stand out. Markup and demand shocks are important drivers of yield curve while technology and monetary policy shocks contribute very little. Demand shocks explain a significant variation of the yield curve fluctuation at all maturities but for the short and medium term forecast horizons, the effect is hump shaped in maturity. At these forecast horizons, demand shocks have the maximum effect on yields at the medium end of the curve. For example, demand shocks explain almost three-quarters of the variation in the 3 year yield in the medium term. On the other hand, markup shocks have the biggest effect on longer maturity yields at all forecast horizons. At the short end of the yield curve, the monetary policy shock contributes a significant share to the variance but its contribu- tion diminishes to essentially zero with the forecast horizon. The contribution of structural shocks to historical U.S. data will be important in determining the effects of re-optimization shocks, as we will discuss in section 3.4.
Product optimization techniques have been used mainly for food products (Norback and Evans, 1983; Lagrange and Norback, 1987; Moskowitz, 1987 and 1988). Linear programming one of the methods for optimization was used by Rust (1976) in formulating preblended meats, Dano (1972) in formulating ice cream. Bender et al. (1982) used this method in selecting a formulation for a least cost mayonnaise. Although Kavanagh ( 1978) used linear programming in paint and resin development, there is little evidence of the use of consumer data in the optimization of non-food products in the area other than skin care products. Glue stick is a product which is new to the Thai market. The price is high compared with other glue products with similar performance. The high cost of raw material is one of the factors which contributes to this high price. Replacing of some synthetic raw materials with natural raw materials not only could produce a cheaper product but also create an environmental friendly and a safe product.
Results of batik production process optimization using PSO method is shown in Table III. As shown in Table III that after the optimization of the production process of batik, the costs of raw materials needed is US$ 671.21 with as many as 104 hours of production time. Optimization results show that there is a saving of raw materials amounted to 14.801%. Results also showed an optimization of production time savings of 116 hours to 105 hours, or 10.345%. These savings would have an impact on improving operational efficiency in the batik industry. This optimization is expected to help strengthen the competitiveness of Yogyakarta batik industry.
Natural immune systems consist of the structures and processes in the living body that provide a defence system against invaders and also altered internal cells which lead to disease. In a glance, immune system’s main tasks can be divided into three parts; recognition, categorization and defence. As recognition part, the immune system firstly has to recognize the invader and foreign antigens e.g. bacteria, viruses and etc. After recognition, classification must be performed by immune systems, this is the second part. And appropriate form of defence must to be applied for every category of foreign aggressive phenomenon as the third part. The most significant aspect of the immune systems in mammals is learning capability. Namely, the immune systems can grow during the life time and is capable of using learning, memory and associative retrieval in order to solve mentioned recognition and classification tasks. In addition, the studies show that the natural immune systems are useful phenomena in information processing and can be helpful in inspiration for problem solving and various optimization problems (Keko et al., 2003). 6.2 Artificial immune system
The optimization problem and parameters may change in different time points, and the decision made at any point in time can affect decisions at later time points (e.g. there can be a capacity constraint defined on 2 months, whereas the planning cycle is 1 month)