Strong Global Search Capability Research Articles

To optimize well pattern during miscible gas injection process via heuristic techniques

To detect optimum well locations for new injection wells one must generally notice to existing well locations, reservoir shape, capital cost per well construction in each area as well as recovery increase relevant to new well layout. Designing injection process presents inverse optimization issue whereas adjusting exploitation wells prior to primary recovery exhibits classic optimization problem. Most important design factor namely injection well layout has no unique explicit solution to solve via analytical or numerical techniques thus one can propose meta or hyper heuristic approaches to solve such problems. Our main novelty in this work is that here we cooperate four heuristic optimization approaches with our self made fast simulation technique to optimize well locations for two exemplar miscible gas injection processes. We employ MATLAB software to construct artificial porous media prototypes with different uncertainties to investigate optimization technique performances in low rather than high risk media. Outcomes illustrate that approaches like artificial immune system or particle swarm optimization which exhibit strong local search can effectively detect optimal well locations in high risk porous media. Results also show that solution techniques such as Monte Carlo that have strong global search capabilities at expense of weak local search can discover optimum well locations only in media with low uncertainty.

An Improved Chaos Sparrow Search Optimization Algorithm Using Adaptive Weight Modification and Hybrid Strategies

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optimal easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, both the solution accuracy and convergence speed are higher than other algorithms. It further indicates that the ICSSOA has an outstanding ability to jump out of the local optimum.

Integrating Hoisting Efficiency into Construction Site Layout Plan Model for Prefabricated Construction

Construction site layout plans (CSLP) are vitally significant to prefabricated construction project management; in particular, abundant hoisting operations reinforce the importance of the location of tower cranes and precast component supply points. However, previous studies on CSLP have been conducted on traditional construction sites, and there is a gap in the research regarding the prefabricated construction site layout plan. This paper attempts to establish a comprehensive CSLP model to optimize the facility layout problem on prefabricated construction sites, integrating the hoisting efficiency of precast components into previous studies on CSLP considering safety risks and transportation cost. To solve the proposed multiobjective model, the Genetic algorithms (GAs) is applied to obtain Pareto optimal solutions due to its strong global search capability. To verify the feasibility and effectiveness of the proposed model, a prefabricated residential building project is studied as a case study. This paper attempts to improve the CSLP model for the scene of prefabricated construction in the preconstruction stage to optimize safety risk, transportation cost, and hoisting efficiency, which may facilitate future research attempting to improve the prefabricated construction site layout considering more complex actual situations.

Photonic crystal topological design for polarized and polarization-independent band gaps by gradient-free topology optimization.

Photonic crystals can be adopted to control light propagation due to their superior band gap feature. It is well known the band gap feature of photonic crystals depends significantly on the topological design of the lattices, which is rather challenging due to the highly nonlinear objective function and multiple local minima feature of such design problems. To this end, this paper proposed a new band-gap topology optimization framework for photonic crystals considering different electromagnetic wave polarization modes. Based on the material-field series-expansion (MFSE) model and the dielectric permittivity interpolation scheme, the lattice topologies are represented by using a small number of design variables. Then, a sequential Kriging-based optimization algorithm, which shows strong global search capability and requires no sensitivity information, is employed to solve the band gap design problem as a series of sub-optimization problems with adaptive-adjusting design spaces. Numerical examples demonstrated the effectiveness of the proposed gradient-free method to maximize the band gap for transverse magnetic field (TM), transverse electric field (TE), and complete modes. Compared with previously reported designs, the present results exhibit less dependency on the guess of the initial design, larger band gaps and some interesting topology configurations.

Application of Coupled LDA–KPCA and BO–MKRVM Model to Predict Coal and Gas Outbursts

This paper proposed a coupled model of effective feature extraction and optimized classifier, which can overcome the existing problems of coal and gas outbursts classification in the literatures. Firstly, we support the use of kernel principal component analysis and linear discriminant analysis methods to extract linear and nonlinear feature information from coal and gas outbursts influencing factors. Secondly, in order to realize the complementarity and correlation between coal and gas outbursts influencing factors, we perform parallel feature fusion to combine the extracted linear and nonlinear feature information. Thirdly, an improved classifier called BO–MKRVM has been proposed that combines mixed kernel relevance vector machine (MKRVM) and Bayesian optimization (BO) algorithm to predict coal and gas outbursts according to the extracted features. In BO–MKRVM model, an effective mixed kernel function which combines Gaussian kernel function and Sigmoid kernel function is proposed to improve the learning and generalization ability of the MKRVM classifier, then the BO and tenfold cross-validation are utilized to optimize kernel parameters and weight coefficients of MKRVM with strong global and local search capability, the proposed BO–MKRVM classifier is performed on coal and gas outbursts dataset. Compared with the single feature extraction method, the combination of linear and non-linear feature extraction methods can obtain complete feature information and contribute to the classification performance of outbursts. The mixed kernel function considers the characteristics of coal and gas outbursts sample data, which can also effectively improve the outbursts accuracy. After the MKRVM classifier is optimized by BO algorithm, the BO–MKRVM classifier has better fitting effect and generalization ability, and obtains higher accuracy with a lower time. The experimental results are obviously better than those of other classification models, which further verifies the applicability of the proposed coupled model.

Topological Design of Freely Vibrating Bi-Material Structures to Achieve the Maximum Band Gap Centering at a Specified Frequency

AbstractThe topological design of structures to avoid vibration resonance for a certain external excitation frequency is often desired. This paper considers the topology optimization of freely vibrating bi-material structures with fixed/varying attached mass positions, targeting at maximizing the frequency band gap centering at a specified frequency. A band gap measure index is proposed to measure the size of the band gap with a specified center frequency. Aiming at maximizing this measure index, the topology optimization problem is formulated on the basis of the material-field series-expansion (MFSE) method, which greatly reduces the number of design variables and at the same time keeps the capability to describe relatively complex structural topologies with clear boundaries. As the considered optimization problem is highly non-linear and may yield multiple local minima, a sequential Kriging-based optimization solution strategy is employed to effectively solve the optimization problem. This solution strategy exhibits a relatively strong global search capability and requires no sensitivity information. With the present topology optimization model and the gradient-free algorithm, relative large band gaps with specified center frequencies have been obtained for two-dimensional (2D) beams and three-dimensional (3D) plates, without specifying the frequency orders between which the desired band gap occurs in prior.

Multi-Objective Reactive Power Optimization Based on Improved Particle Swarm Optimization With ε-Greedy Strategy and Pareto Archive Algorithm

This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of $\varepsilon $ -greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II).

Solving Power Economic Dispatch Problem with a Novel Quantum-Behaved Particle Swarm Optimization Algorithm

This paper proposes the shrink Gaussian distribution quantum-behaved optimization (SG-QPSO) algorithm to solve economic dispatch (ED) problems from the power systems area. By shrinking the Gaussian probability distribution near the learning inclination point of each particle iteratively, SG-QPSO maintains a strong global search capability at the beginning and strengthen its local search capability gradually. In this way, SG-QPSO improves the weak local search ability of QPSO and meets the needs of solving the ED optimization problem at different stages. The performance of the SG-QPSO algorithm was obtained by evaluating three different power systems containing many nonlinear features such as the ramp rate limits, prohibited operating zones, and nonsmooth cost functions and compared with other existing optimization algorithms in terms of solution quality, convergence, and robustness. Experimental results show that the SG-QPSO algorithm outperforms any other evaluated optimization algorithms in solving ED problems.

Wind driven dragonfly algorithm for global optimization

SummaryDragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

Guide Circle-based Improved Ant Colony Algorithm

In order to solve the path planning problem of autonomous mobile robots in an unknown environment, the traditional ant colony algorithm and the guide circle algorithm were combined, and an improved ant colony algorithm based on the guide circle was proposed. In terms of local path planning, the guide circle algorithm is used to avoid obstacles, which effectively solved the problem of unstable path planning ability in narrow spaces. In the aspect of global path planning, the concept of safety index circle is introduced according to the length between robot and obstacle, and partial heuristic information of ant colony algorithm is improved, so as to avoid the traditional ant colony algorithm falling into local optimal. It is verified by simulation experiments that the improved algorithm has a stronger global search capability, faster convergence speed, and enhanced path planning capability.

Complex permittivity inversion algorithm with adaptive learning strategy and parameter balancing mechanism

In this paper, we studied an adaptive learning particle swarm optimization (ALPSO) algorithm for complex permittivity inversion problem. Adaptive learning strategy and parameter balancing mechanism were used in the algorithm. The candidate particle generated in the adaptive learning strategy competed with the global optimal particle, which enabled the population to maintain strong global search capability and high search accuracy. A parameter balancing mechanism was proposed for complex permittivity inversion of low-loss materials. The inversion results of three samples showed that ALPSO not only had fast convergence speed but also was not easy to fall into the local optimum. The objective function improvement rate was 10.91 %, 1.21 % and 37.53 %, respectively. The reflectivity measurement results of the composite sample were in good agreement with the theoretical calculation results, which proved the correctness of the inversion results.

Thrust Performance Improvement for PMSLM Through Double-Layer Reverse Skewed Coil and WRF-MKH Method

This article is dedicated to achieving the best thrust performance of high thrust and low thrust ripple of permanent magnet synchronous linear motors (PMSLMs), which are used in precision positioning system. To achieve this, a novel PMSLM with double-layer reverse skewed coil (DRSC) is designed and optimized. First, the topological structure of the DRSC-PMSLM is introduced, and the effectiveness of the DRSC is analyzed qualitatively according to the analytic model. Certain hypotheses that are more suited to qualitative analysis can lead to the low accuracy of the analytic model. Therefore, for the subsequent optimization, the weighted random forest (WRF), an enhanced random forest (RF) algorithm by assigning different weights to different regression trees, is proposed to fit the sample data generated by the finite element method to establish a high-precision proxy model. Accuracy test proves that the WRF model has higher accuracy than the standard RF model and the analytical model. Subsequently, a new global optimization algorithm, call the modified Krill Herd (MKH) algorithm is proposed to iteratively optimize the WRF model to obtain the optimal structure parameters. The MKH algorithm is a modified KH algorithm that two strategies are used to improve the convergence rate and avoid premature convergence. Comparative experiments are performed by using genetic algorithm, particle swarm optimization, and standard Krill herd, which prove that the MKH has faster convergence speed and stronger global search capability. The thrust performance of the optimized motor is improved considerably compared with that of the initial motor, which further proves the effectiveness of the MKH used in this article. Finally, the prototype experiment proves that the PMSLM with DRSC has the best thrust performance.

Optimal hydropower station dispatch using quantum social spider optimization algorithm

SummaryIn this article, a new quantum social spider optimization (QSSO) algorithm is proposed. In the QSSO algorithm, we introduce an encoding approach based on bits described on social spider optimization (SSO) and serves as the evolution method of the population space. For the encoding of individuals, the probability amplitude expression of quantum bit is applied to describe the position of individuals, by which one individual's position can be expressed as the superposition of multistates. In such a way, the population diversity and the global searching capability of the SSO algorithm are enhanced. The QSSO algorithm was used to optimize the hydropower station dispatch, and the calculation results show that QSSO algorithm has fast convergence, small number of tuning parameters, high calculation accuracy, stability, simple, and is easy to be implemented with strong global search capability.

Clonal selection based intelligent parameter inversion algorithm for prestack seismic data

Amplitude variation with offset (AVO) elastic parameter inversion is an approach of oil exploration that employs seismic information, and it is a problem of non-linear optimization. When using a quasi-linear or linear approach to solve the problem, the inversion result is unreliable or inaccurate. Metaheuristic search methods, e.g., bio-inspired optimization algorithms such as genetic algorithms, are capable of handling highly non-linear optimization problems and thus provide a promising approach for oil and gas exploration. As one of the metaheuristic search approaches, the immune clone selection algorithm exhibits the property of fast convergence and strong global search capability. In this paper, the immune clone selection algorithm is used to address the problem of AVO elastic parameter inversion. This algorithm employs the specific initialization strategy of Aki as well as the approximation equation of Rechard, which is utilized in the elastic parameter inversion process to smooth the initialization parameter curve. Additionally, the genetic operation in the algorithm is improved in accordance. The results of multiple experiments demonstrate that the approach could significantly improve the inversion accuracy, and the correlation coefficient of the elastic parameters acquired via inversion is specifically high.

Static Schedule Generation for Time‐Triggered Ethernet Based on Fuzzy Particle Swarm Optimization

In time-triggered ethernet (TTEthernet), designing and optimizing the static scheduling of TT messages to improve the real-time performance of the whole network control system is important. When there is a high load on TTEthernet network communication, the conventional static scheduling policy introduces certain problems, such as increased packet loss rate, load imbalance, and transmission delay. Meanwhile, basic artificial intelligence algorithms generally features convergence within only a few steps, leading to higher probability to fall into a local optimal solution. To realize these targets, Fuzzy particle swarm optimization (FPSO) based on population diversity is established. By combining Particle swarm optimization (PSO) with a fuzzy algorithm, setting the adjustment of the inertia weight and the regulation of the mutation factor as the controlled variables of the Fuzzy logic controller (FLC), and adjusting the FLC inference rules, a novel static schedule generation for TTEthernet is proposed. Simulation results prove that, compared to the conventional rate-monotonic scheduling algorithm and the PSO algorithm, FPSO has a strong global search capability when the communication of the TTEthernet system is in a high-load state. FPSO improves the load balancing of the network and reduces the transmission delay of TT message packets. FPSO shows excellent ability to optimize scheduling tables and guarantee the real-time performance of the TTEthernet system.

Optimized least-squares support vector machine for predicting aero-optic imaging deviation based on chaotic particle swarm optimization

In the time-averaged flow field, aero-optic imaging deviation (AOID) and its influencing variables are connected by complicated and strongly coupled nonlinear time-dependent relationships that present many difficulties for fast calculation of AOID. To achieve real-time online AOID compensation in practice, chaotic particle swarm optimization (CPSO) and the least-squares support vector machine (LSSVM) are combined to construct a predictive AOID model for supersonic aircraft in flight. With its strong global-search capability, the CPSO algorithm is used to optimize the parameters of the LSSVM predictive model. First, a chaotic sequence is used to initialize the particle positions, thereby enhancing search diversity. The premature convergence of normal particle swarm optimization (PSO) is then countered by using CPSO. If PSO falls into a local optimum, the extreme position of the population is adjusted and the current search trajectory of the particles is disturbed so that the particles search new neighborhoods and paths, thereby increasing the probability of escaping the local optimum. The simulation results show that the predictive accuracy of the CPSO-LSSVM model exceeds that of the LSSVM model alone or the back-propagation neural network model. The CPSO-LSSVM model is effective at compensating for AOID within a limited range, and it avoids the disadvantages of traditional geometrical-optics calculations, namely that they are time-consuming, laborious, and error-prone. When the training data of LSSVM is enough, any AOID can be estimated by the CPSO-LSSVM model.

Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions

A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO’s strong global search capability and LS’s fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants.

Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm

Precise prediction of coal thickness is of the utmost importance in realizing intelligent and unmanned mining. As the channel wave is characterized by an easily recognizable waveform, a long propagation distance, and strong energy, it is widely used for coal thickness inversion. However, most traditional inversion methods are local in nature, and the inversion result is probably not optimal in the global scope. This paper introduces the GA-SIRT hybrid approach, which combines Genetic Algorithms (GA) and Simultaneous Iterative Reconstructive Techniques (SIRT) in order to deal with the above problem and to improve the accuracy of coal thickness inversion. The proposed model takes full advantage of the strong global search capability of GA and of the fast local convergence rate of the SIRT. Moreover, it inhibits the poor local search ability and the local optimal value effect of the GA and the SIRT respectively. The application of the GA-SIRT in the Guoerzhuang coal mine has significantly enhanced its accuracy, stability, and overall computational efficiency. Hence, the introduced novel hybrid model can precisely resolve and identify the coal thickness according to the channel wave. It can also be extended to other geophysical tomographic inversion problems towards the reduction of potential local optimal solutions.

Anomaly Detection Algorithm Based on FCM with improved Krill Herd

The anomaly detection algorithm plays an important role in the field of network security. Among them, the most representative method is anomaly detection based on fuzzy C-means (FCM). FCM relies heavily on the initial clustering center and is prone to local extremum. Therefore, the detection effect of the FCM-based anomaly detection algorithm is not ideal in some cases. The herd intelligent optimization technology has a strong global search capability and is widely used in various fields. As a herd intelligence technology, the krill herd algorithm has a relatively simple optimization function structure, which has strong global search ability and is easy to integrate with other optimization strategies. Therefore, a KH algorithm with strong global search capability is introduced, and a hybrid KH-FCM algorithm is proposed. In the hybrid KH-FCM algorithm, the randomly generated initial population will be divided into two subpopulations containing the same number of individuals.

Optimal planning of energy storage system using modified differential evolution algorithm

In smart distribution network, optimal planning of Energy Storage Systems (ESS) can lead to voltage profile improvement, network loss reduction and increasing the profit of distribution company (DISCO). This paper proposes the application of Modified Differential Evolution Algorithm (MDEA) which is very strong global search capability for maximizing the DISCO profit over the planning. The objective function is the difference between the benefit which is the energy arbitrage in a deregulated market and the costs which are investment cost, operation and maintenance cost. The implementation of MDEA is performed on a 30-bus radial distribution network. The simulation results demonstrate that the proposed MDEA application significantly increases the profit without any violation of network constraints.