A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

This paper addresses the m-machine no-wait Flow Shop Scheduling with Setup Times (NW-FSSWST). Two performance measures: total flow time and makespan are considered. The objective is to find a sequence that minimizing total flow time ($$\sum C_{j}$$ ∑ C j ) and makespan ($$C_{j}$$ C j ) simultaneously. A Hybrid Genetic Local and Global Search Algorithm (HGLGSA) is proposed to solve the NW-FSSWST for two performance criteria. The hybrid genetic algorithm is constructed by insert-search and self-repair algorithm with self-repair function. The proposed HGLGSA is tested on 192 benchmark problems of NW-FSSWST in the literature. A full factorial experimental design is made for determined the best parameter sets that improve the performance of the proposed algorithm. The computational results are compared with the benchmark solutions from the literature. The experimental results demonstrate the effectiveness and efficiency of the proposed HGLGSA for solving NW-FSSWST.

Application of the global search algorithm to analyze the kinetic mechanism of the thermal decomposition of flexible polyurethane foam

Accurate thermal decomposition mechanism and kinetic parameters are helpful to analyze the combustion process of flexible polyurethane foam. The thermal decomposition process of flexible polyurethane foam products (amine derivatives) was ignored in the past. Three thermal decomposition mechanisms of flexible polyurethane foam were proposed according to the thermogravimetry experiment of flexible polyurethane foam in the nitrogen atmosphere, two of which included the thermal decomposition of amine derivatives. The global search algorithm was proposed to estimate the kinetic parameters of the thermal decomposition of solid material. The results show that the global search algorithm is efficient and accurate in estimating kinetic parameters. The results also show the thermal decomposition mechanism including the carbodiimide and polycarbondiimide can well describe the thermal decomposition process of flexible polyurethane foam and amine derivatives. The activation energy, pre-exponential factor, and reaction order of flexible polyurethane foam are 187.3 kJ mol−1, 1015.6 s−1, and 1.22, respectively.

Anion photoelectron spectroscopy and quantum chemistry calculations of TaSi16ˉ/0 clusters: Global minimum fullerene-like cage structure, bonding and superatom properties

Anion photoelectron spectroscopy combined with quantum chemistry calculations and particle swarm optimization (CALYPSO) global search algorithm were used to investigate the structural and bonding properties of TaSi16ˉ/0 clusters. The vertical...

Path Planning Algorithm of Dijkstra-Based Intelligent Aircraft under Multiple Constraints

Aiming at the rapid planning of the optimal flight path of the intelligent aircraft, considering the error constraints and correction probability constraints, a model for intelligent aircraft path planning under multiple constraints is constructed, and a global search algorithm based on Dijkstra algorithm is proposed to solve the model. By calculating the residual error and restricts flight distance, the basic Dijkstra algorithm is improved to make it more adaptable to solve the path planning under multiple constraints. At the same time, simulation experiment is conducted with the optimal goal of the shortest track length and satisfying the error constraints. The experimental results show that the aircraft passed a total of 18 correction points when it reached the destination. The total track length was 144 287.932 m, the vertical position error was 17.254 units, and the horizontal position error was 6.420 units. The results meet the error requirements. The results show that the intelligent aircraft path planning model and Dijkstra-based global search algorithm with multiple constraints are reasonable in solving such problems.

Application of focal-time analysis for improved induced seismicity depth control: A case study from the Montney Formation, British Columbia, Canada

Characterization of induced seismicity and associated microseismicity is an important challenge for enhanced oil recovery and development of tight hydrocarbon reservoirs. In particular, accurately correlating hypocenters of induced events to stratigraphic layers plays an important role in understanding the mechanisms of fault activation. Existing methods for estimating focal depth, however, are prone to a high degree of uncertainty. A comprehensive analysis of inferred focal depths is applied to induced events that occurred during completions of horizontal wells targeting the Montney Formation in British Columbia, Canada. Our workflow includes a probabilistic, nonlinear global-search algorithm (NonLinLoc), a hierarchical clustering algorithm for relative relocation (GrowClust), and depth refinement using the recently developed focal-time method. The focal-time method leverages stratigraphic correlations between P-P and P-S reflections to eliminate the need for an explicit velocity model developed specifically for hypocenter depth estimation. We find that this approach is robust in the presence of noisy picks and location errors from epicenters obtained using a global-search algorithm, but it is limited to areas where multicomponent 3D seismic data are available. We have developed a novel method to determine statics corrections to ensure that the passive seismic observations and 3D seismic data share a common datum in areas of moderate to high topography. Our results highlight the importance of transverse faults, which appear to provide permeable pathways for activation of other faults at distances of up to 2 km from hydraulic fracturing operations.

Design of Warship Simulation Using Variable-Chromosome Genetic Algorithm

A genetic algorithm (GA) is a global search algorithm based on biological genetics. GAs are generally used for industrial applications, artificial neural networks, web applications, the defense industry, and so on. However, it is difficult to apply GAs to more complex situations because of the fixed number of chromosomes. In this research, in order to overcome this limitation, we propose a variable-chromosome GA with a chromosome attachment feature. Verification of the algorithm is carried out through anti-submarine high value unit (HVU) escort mission simulations. Ultimately, it is confirmed that the GA using the variable chromosome is more effective in dealing with highly complex missions, whereby the number of chromosomes gradually increases.

A Global Search Algorithm for Determining Water Influx in Naturally Fractured Reservoirs

The determination of water influx in naturally fractured reservoirs is always a significant and difficult task in gas reservoir engineering. To improve this situation, this paper presents a new global search algorithm to determine water influx in the naturally fractured gas reservoirs. In the methodology, a dimensionless water influx derivative curve is first introduced in this paper. It is used to identify flow regimes of water invasion by combining with the water influx characteristic curve. Following that, a sensitivity analysis is performed to study the impacts of key factors on flow regimes. Finally, based on the sensitivity study and material balance equation, a global search algorithm is proposed to obtain water influx. Results show that there are two steps in the dimensionless water influx curve and a “V-shape” in the derivative curve. The smaller the aquifer and gas reservoir radius ratio is, the earlier and more obvious the “V-shape” appears. The smaller the storativity ratio is, the earlier the “V-shape” appears. The smaller the interporosity flow coefficient is, the more obvious the “V-shape” is. Results of the field application demonstrate the method applicability, which provide a good reference for further work about determination of water influx.

The Automatic Detection of Diabetes Based on Swarm of Fish

Diabetes is a major health problem and a disease that can be very dangerous in developing and developed countries, and its incidence is increasing dramatically. In this chapter, the authors propose a system of automatic detection of diabetes based on a bioinspired model called a swarm of fish (fish swarm or AFSA). AFSA (artificial fish swarm algorithm) represents one of the best methods of optimization among swarm intelligence algorithms. This algorithm is inspired by the collective, the movement of fish and their different social behaviors in order to achieve their objectives. There are several parameters to be adjusted in the AFSA model. The visual step is very significant, given that the fish artificial essentially moves according to this parameter. Large parameter values increase the capacity of the global search algorithm, while small values tend to improve local search capability. This algorithm has many advantages, including high speed convergence, flexibility, and high accuracy. In this chapter, the authors evaluate their model of AFSA for the purpose of automatic detection of diabetes.