A hybrid gene expression programming algorithm based on orthogonal design

Design and multi-objective optimization of hairpin motor cooling system based on orthogonal design method and composite algorithm

In the paper, the analysis of wave scattering on a system of two soundproof spheres was carried out using the orthogonal central compositional design method. The main objectives of the work are to determine the main parameters, the change of which significantly affects on the whole system; to determine the variable parameter values of the system, at which the loss functions (the total scattering cross section and the normalized pressure at a fixed point) take the minimum and maximum values, and to study the system at optimal parameter values. The orthogonal compositional design method is implemented for a three-factor computational experiment, where three main parameters of the system were selected as factors: the wave radius, the ratio of the sphere radii, and the distance between the sphere centers. For the obtained regression equation, the significance of the coefficients was checked using Student’s t-test and the adequacy of the model using Fisher’s F-test. It was found that in the case of a spherical wave from a monopole radiation source, the nonlinear influence of the factors is more pronounced than in the case of a plane wave; normal pressure calculated at a fixed point is insensitive to changes in parameters in the case of a plane wave, as well as in the case of a monopole radiation source for small values of the sphere surface complex conductivity; the total scattering cross section is highly sensitive in the case of a system with ideally rigid spheres; the highest and lowest values of the loss functions are achieved at the boundary of the three-dimensional parametric region of factors changes. Pressure distribution diagrams were constructed, which made it possible to determine the zones of high or low pressure for the found optimal values of the factors.

In many industries including metallurgy, petrochemicals, paper, aerospace, pharmaceutical, electronics, ceramic and automotive manufacturing, the production system can contain a machine bottleneck which processes, in some cases, all the jobs. The management of this machine is crucial for the enterprise because it is responsible for delays in customers' orders delivery. The jobs scheduling on this machine is a way of approaching the problem and treats the whole production system. The single machine model provides a basis for more complicated environment such those seen in many real cases. Also, several studies have shown that several jobs, if not all, have time-dependent setup times. So, decision makers must therefore organize the jobs scheduling on this single machine by trying to minimize downtime while respecting the different deadlines. On the other hand, several resolution methods have been developed in literature to solve this kind of problem using approaching methods like metaheuristics or hybrid methods integrating exact methods. This thesis is in this direction. In this research, we propose several effective approaches to solve the single machine scheduling problem with sequence-dependent setup times with the objective to minimize total tardiness of the jobs. First, a new crossover operator in a genetic algorithm is proposed. This algorithm shows its ability to be competitive with the best approaches found in the literature on benchmark problems. We thus highlight the importance of incorporating specific problem knowledge into genetic operators, even if classical genetic operators could be used. However, the results obtained show that the proposed genetic algorithm is less efficient in comparison to the best resolution method found in the literature. Indeed, we noticed that the proposed algorithm lacks an intensification process for enhancing its. To overcome this shortcoming, we explore resolution class methods that showed promising prospects in the last decade. Even, the hybrid algorithms have achieved very good results in a wide variety of problems. In recent years, several researchers have introduced the hybridization of metaheuristics with exact methods. In fact, this kind of hybridization can be an attractive alternative because both methods have very different characteristics that can be combined to produce better results. Thus we explore the design of hybrid genetic algorithms improving the intensification process of the proposed genetic algorithm by integrating an exact method and mechanisms from other resolution methods to improve the performance. However, we find few approaches hybridizing metaheuristics and constraint programming in the literature to solve scheduling problems. In a second step, we propose a modeling for the total tardiness single machine with setup-dependent times scheduling problem using the constraint-based scheduling and the ILOG C++ API. The related experiments show that algorithm efficiency improves with specific tree search strategies and heuristic scheduling variables. Under prohibitive time computing, the proposed approach results are generally far from the best known solutions. However, we got interesting performance on small instances to consider allowing its hybridization with other resolution methods. In a third step, we introduce a hybrid approach based on the integration between genetic algorithm and constraint based scheduling approaches. This latter approach is integrated in the reproduction and the intensification processes of a genetic algorithm autonomously. Finally, we propose a hybrid algorithm which integrates several concepts from constraint programming, multi-objective optimization and ant colonies optimization in a crossover operator into a genetic algorithm. The proposed hybrid algorithms show excellent performance on benchmark problems from the literature by improving many of the best known solutions for some of them.

A hybrid algorithm of particle swarm optimization (PSO) and local search (LS) is proposed to solve the redundancy allocation problem for series-parallel multi-state systems. The proposed hybrid algorithm is able to design the system structure with a minimum cost to provide a desired level of availability. Unlike most of the previous studies that only consider homogeneous redundancy, the proposed algorithm allows for the heterogeneous redundancy technique. The universal generating function method is applied to evaluate system availability. The standard PSO is modified and novel LS strategies are integrated to adapt to the redundancy allocation problem. Case studies that facilitate comparisons between the proposed hybrid algorithm and other non-hybrid heuristics as well as meta-heuristics reported in the literature (such as genetic algorithms, tabu search, and ant colony optimization) are provided. The results illustrate the advantages of the proposed hybrid algorithm in terms of the solution quality or algorithm efficiency.

This paper attempts to enhance the learning performance of radial basis function neural network (RBFN) through swarm intelligence methods and self-organizing map (SOM) neural network (SOMnet). Further, the particle swarm optimization (PSO) and genetic algorithm (GA)-based method (i.e., PG approach) is employed to train RBFN. The proposed SOMnet + PG approach (called: SPG) algorithm combines the automatically clustering ability of SOMnet with PG approach. The simulation results revealed that SOMnet, PSO, and GA methods can be integrated ingeniously and redeveloped into a hybrid algorithm which aims for obtaining the best accurate learning performance among other algorithms in this study. On the other hand, method evaluation results for two benchmark problems and a gold price prediction case showed that the proposed SPG algorithm outperforms other algorithms and the auto-regressive integrated moving average (ARIMA) models in accuracy.

Orthogonal design method (ODM) is widely used in real world application while it is not used for antenna design yet. It is employed to optimize roughly designed antenna in this paper. The geometrical factors of the antenna are relaxed within specific region and each factor is divided into some levels, and the performance of the antenna is constructed as objective. Then the ODM samples small number of antennas over the relaxed space and finds a prospective antenna. In an experiment of designing ST5 satellite miniantenna, we first get a roughly evolved antenna. The reason why we evolve roughly is because the evolving is time consuming even if numerical electromagnetics code 2 (NEC2) is employed (NEC2 source code is openly available and is fast in wire antenna simulation but not much feasible). Then the ODM method is employed to locally optimize the antenna with HFSS (HFSS is a commercial and feasible electromagnetics simulation software). The result shows the ODM optimizes successfully the roughly evolved antenna.

On-line conversion and determination of artemisinin and its kinetic parameters using orthogonal design by coupling of flow injection with capillary electrophoresis

Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimize the overall execution time. The main issue is how jobs are partitioned in which total finishing time and waiting time is minimized. Minimization of these two criteria simultaneously, is a multi objective optimization problem. There are many variations of this problem, most of which are NP-hard problem, so we must rely on heuristics to solve the problem instances. Many heuristic-based approaches have been applied to finding schedules that minimize the execution time of computing tasks on parallel processors. Particle swarm optimization (PSO) is currently employed in several optimization and search problems due to its ease and ability to find solutions successfully. A variant of PSO, called as improved particle swarm optimization (ImPSO) has been developed in this paper and is hybridized with the ant colony optimization (ACO) to achieve better solutions. The proposed hybrid algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. In addition hybrid algorithm using improved particle swarm optimization (ImPSO) with artificial immune system (AIS) is also implemented for the same set of problems to compare with the proposed hybrid algorithm (ImPSO with ACO). It was observed that the proposed hybrid approach (Improved PSO with ACO) gives better results in experiments and reduces finishing and waiting time simultaneously. Key words: Particle swarm optimization (PSO), improved particle swarm optimization (ImPSO), ant colony optimization (ACO), job scheduling, finishing time, waiting time.

Surface shape design in fluid flow problems via hybrid optimization algorithms

As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.

A high specific speed centrifugal pump is used in the situation of large flow and low head. Centrifugal pump parameters need to be optimized in order to raise its head and efficiency under off-design conditions. In this study, the orthogonal experiment design method is adopted to optimize the performance of centrifugal pump basing on three parameters, namely, blade outlet width b2, blade outlet angle β2 and blade wrap angle φ. First, the three-dimensional model of the centrifugal pump is established by CFturbo and SolidWorks. Then nine different schemes are designed by using orthogonal table, and numerical simulation is carried out in CFX15.0. The final optimized combination of parameters is b2 = 24 mm, β2 = 24°, φ = 112°. Under the design condition, the head and efficiency of the optimized centrifugal pump are appropriately improved, the increments of which are 0.74 m and 0.48%, respectively. However, the efficiency considerably increases at high flow rates, with an increase of 6.9% at 1.5 Qd. The anti-cavitation performance of the optimized centrifugal pump is also better than the original pump. The results in this paper can provide references for parameter selection (b2, β2, φ) in the centrifugal pump design.

The flow field inside cyclone separator is a kind of complex three-dimensional rotation turbulent flow with two-phase separation phenomenon. In order to optimize the structure and shorten the cycle of research and development, it is very essential to find the appropriate calculation method for the flow field inside cyclone separator. In this article, the internal flow field of tangential inlet cyclone separator was simulated and calculated by adopting different calculation model and different numerical simulation scheme by orthogonal design method. And the result was compared with that of existing experiment to find a reasonable method for the simulation of the flow field inside the cyclone. On the basis of standard k-ε model, the numerical simulation scheme was formulated by the orthogonal test method, through the comparison of results, the best combinations are gained; This research result has certain reference value for cyclone separator design and performance optimization.

Fast convergence optimization model for single and multi-purposes reservoirs using hybrid algorithm

Use of an orthogonal design method to study two-stage chemical carcinogenesis in BALB/3T3 cells.

Hybrid design of PID controller for four DoF lower limb exoskeleton