Crisscross Optimization Algorithm Research Articles

A new combined transient electromagnetic noise reduction method of VMD-SVD-WTD based on improved dung beetle optimization algorithm with multi-strategy fusion

Abstract The transient electromagnetic method (TEM) is now commonly used in the detection of coal mined-out areas, but it is susceptible to various sources of noise during field site probing, significantly impacting the accuracy of detection. Variational mode decomposition (VMD) has gained widespread adoption for eliminating noise in TEM signals. However, a single VMD may not effectively address both full-band noise and residual Gaussian white noise. To address this limitation, the singular value decomposition (SVD) and wavelet threshold denoising (WTD) are introduced correspondingly. Furthermore, this paper tackles challenges related to parameters selection in VMD and traditional optimization algorithms by proposing the integration of dung beetle optimization (DBO) enhanced with circle chaotic mapping, random walk strategy, and crisscross optimization algorithm (CRCDBO). Consequently, a new combined TEM noise reduction method, VMD-SVD-WTD based on CRCDBO is presented. Through comprehensive simulation tests and comparative analyses, the signal-to-noise ratio of this method is improved by 12.79% compared with VMD and the RMSE is 0.0011, which demonstrate the superiority and effectiveness of VMD-SVD-WTD based on CRCDBO in noise reduction. Meanwhile, this method is applied to an iron ore mine in Shanxi, which can accurately monitor the location of the mined-out area and has good application value.

Optimal Power Flow Calculation Based on Improved Crossover Algorithm

Abstract Aiming at the optimization problem of power flow distribution in power system, this paper puts forward a method for calculating the optimal power flow of power network based on the improved crisscross algorithm. The fast convergence crisscross optimization algorithm (FCSO) is based on the traditional crisscross optimization algorithm (CSO) to improve the efficiency and accuracy of solving the Optimal Power Flow, OPF) problem. Based on the original double crossover operator, the improved algorithm puts forward a brand-new operator-central crossover operator, which alternates with transverse crossover operator according to certain rules, In order to improve the quality of single search and accelerate convergence, each individual in the population performs crossover operation with the current optimal individual in turn, and then performs competition operator to selectively approach the current global optimal individual. Finally, combined with experimental verification, the optimal power flow calculation of reactive power optimization based on FSCO has obvious practical effects in improving network voltage quality and reducing line loss, The average voltage increase ratio of 220kV bus and 110kV bus is 0.676% and 0.855% respectively, and the loss reduction rate reaches 3.51%, Compared with CSO, the convergence and calculation speed of FSCO proposed in this paper is faster, the convergence curve is smoother, and the average time consumption is increased to 59.53% compared with CSO, In the case of obtaining the same precision solution, the optimization time is obviously shortened, the timeliness of the optimal solution in regulation operation is effectively improved, and the problem of reactive power optimization in power system can be well solved.

Research on Economic Load Dispatch Problem of Microgrid Based on an Improved Pelican Optimization Algorithm.

This paper presents an improved pelican optimization algorithm (IPOA) to solve the economic load dispatch problem. The vertical crossover operator in the crisscross optimization algorithm is integrated to expand the diversity of the population in the local search phase. The optimal individual is also introduced to enhance its ability to guide the whole population and add disturbance factors to enhance its ability to jump out of the local optimal. The dimensional variation strategy is adopted to improve the optimal individual and speed up the algorithm's convergence. The results of the IPOA showed that coal consumption was reduced by 0.0292%, 2.7273%, and 3.6739%, respectively, when tested on 10, 40, and 80-dimensional thermal power plant units compared to POA. The IPOA can significantly reduce the fuel cost of power plants.

Solving large-scale combined heat and power economic dispatch problems by using deep reinforcement learning based crisscross optimization algorithm

The extensive interconnection in bulk power systems brings great challenges in the economic dispatch problems, especially the large-scale combined heat and power economic dispatch (CHPED), which is quite intractable due to complex thermal and electrical couplings in cogeneration units. Most meta-heuristic algorithms cannot work well while considering non-convexity, discontinuity, and non-differentiability. Particularly, when it comes to the high dimensional and large-scale CHPED problems, these algorithms are either easy to trap in the local optimum or quite time-consuming. To address these issues, a novel deep reinforcement learning (DRL) based crisscross optimization (CSO) algorithm is proposed for the first time, which can (1) improve the searching ability of CSO, (2) enhance the overall searching efficiency, and (3) perform extraordinarily well for the large-scale CHPED problems. Firstly, to further improve the global search ability, DRL is employed to reduce dimensionality and narrow the search space of the initial population of the CSO algorithm. Then, DRL is updated by the deep deterministic policy gradient (DDPG) strategy, which can facilitate an instantaneous decision in accelerating CSO. Thereafter, by adopting each decision provided by DRL, a great difference may occur in the same scenario to speed up the crossover procedure and guide the upcoming search round. On this basis, the enhanced CSO algorithm could quickly carry out crossovers with search space contraction and dimensionality reduction. Finally, experiments on four test systems have substantiated the effectiveness of the proposed algorithm, especially the superiority over other state-of-the-art techniques in solving large-scale CHPED problems.

A novel multi-agent based crisscross algorithm with hybrid neighboring topology for combined heat and power economic dispatch

Combined heat and power economic dispatch (CHPED) is a challenging optimization problem with characteristics like non-convexity, discontinuity, and non-differentiability. Although the crisscross optimization (CSO) algorithm can alleviate the premature convergence faced by most swarm optimization algorithms, it has a slow convergence speed to approximate the global optimum, especially at the late period of evolutionary process. To address the issue, a novel hybrid neighboring topology based multi-agent crisscross algorithm (HNT-MACSO) is proposed to enhance the balance ability of exploration and exploitation. First, based on the graph theory, the population particles of CSO are structured with two topologies, i.e., the random topology and the small world topology respectively. Second, a hybrid neighboring topology is established by applying the information relay register, aiming to improve the robustness of CSO. Third, the separate CSOs assigned to different topologies are deployed on a multi-agent system (MAS), which enables a flexible and robust distributed evolving environment for all agents to search in an independent and asynchronous optimization manner. Furthermore, five cogeneration systems are tested, and experimental results show that the proposed HNT-MACSO outperforms other state-of-the-art algorithms in terms of solution accuracy and runtime, which confirms the effectiveness and superiority of HNT-MACSO for large-scale CHPED problems.

Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang–Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article.

Comfort Prediction of Office Chair Surface Material Based on the ISSA-LSSVM

This study serves the purpose of assisting users in selecting a comfortable seat surface material for office chairs and enhancing users' comfort while using office chairs. To address the issue that the selection of traditional seat surface material is too subjective and that the prediction effect is poor, an improved sparrow search algorithm (ISSA) optimized least squares support vector machine (LSSVM) method for office chair seat surface material comfort prediction has been proposed. Sparrow Search Algorithm (SSA) was optimized with Sobol sequences, nonlinear inertial weights, and a crisscross optimization algorithm to produce the Improved Sparrow Search Algorithm (ISSA), and then the relevant parameters of the LSSVM algorithm were optimized with the modified algorithm to improve its prediction performance. The prediction accuracy of the ISSA-LSSVM model is as high as 95.75% by combining the body pressure distribution experiments; the root mean square error (RMSE) is 0.29; the goodness of fit (R2) is 0.92; the mean absolute error (MAE) is 0.24; the standard deviation (RSD) is 5.99%. The ISSA-LSSVM model predicts seat surface material comfort more accurately and reliably. This strategy can assist consumers to narrow down their seat surface material choices and even suggest an optimal selection. In this way, it can boost users' pleasure with office chairs, which has great potential for wide application.

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection.

The online version contains supplementary material available at 10.1007/s42235-022-00298-7.

A service composition method using improved hybrid teaching learning optimization algorithm in cloud manufacturing

In the cloud manufacturing process, service composition can combine a single service into a complex service to meet the task requirements. An efficient service composition strategy is crucial, as it affects the efficiency of resource and capacity sharing in the cloud manufacturing system. However, in the face of a large-scale environment, the existing methods have the problems of slow convergence and instability. To solve the problems above, we propose an improved optimization method, named improved-TC. Specifically, we are inspired by the horizontal crossover of CSO in the hybrid-TC teaching phase, the Hybrid-TC is proposed in our previous work, which is a hybrid of the teaching-learning-based optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). Improved-TC is an improvement on the learning phase of hybrid-TC algorithm, we change the search method of hybrid-TC in the learning phase to a one-dimensional search, thereby some dimensions in the population that are trapped in the local optimum have the chance to jump out of the iteration. Experiments show that our proposed method has a faster convergence speed and more stability in the face of service composition in large-scale environments.

A hybrid Aquila optimizer and its K-means clustering optimization

Aiming at the defects of the Aquila optimizer (AO) in dealing with some complex optimization problems, such as slow convergence speed, low convergence accuracy, and easy to fall into local optimum, in this paper, a hybrid Aquila optimizer (HAO) algorithm based on Gauss map and crisscross operator is proposed. First, Gauss map is introduced to initialize the Aquila population to improve the quality of the initial population. Then use the crisscross operator to promote the exchange of information within the population and maintain the diversity of the population in each iteration, which not only enhances the ability of the algorithm to jump out of the local optimum but also accelerates the global convergence of the algorithm. The results of experiments using 21 classical benchmark functions indicate that HAO has better global search ability, faster convergence speed, and better stability than AO. The overall optimization performance of HAO in different dimensions is better than particle swarm optimization (PSO) algorithm, gray wolf optimization (GWO) algorithm, whale optimization algorithm (WOA), and crisscross optimization (CSO) algorithm. Finally, the results of K-means clustering optimization on six University of California (UCI) standard data sets demonstrate that HAO has significant advantages over three algorithms that are good at clustering optimization.

Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy

Multi-area economic dispatch (MAED) is a non-convex non-differentiable high-dimensional optimization problem, aiming to minimize the total fuel cost and meet the requirements of load balance. Current distributed mathematical programming methods are hard to solve the valve point effect in MAED. In addition, distributed meta-heuristic algorithms so far require a centralized controller. To address these issues, a decoupled distributed crisscross optimization with population cross generation (DDCSO-PCG) algorithm is proposed, which can 1) realize decentralization, 2) protect area data privacy, 3) reduce dimensions and improve convergence ability. First, the population cross generation (PCG) strategy is integrated into the crisscross optimization (CSO) algorithm to maintain population diversity and enhance exploitation ability. Second, the DDCSO-PCG algorithm is implemented to solve the MAED problem in a fully decentralized manner. Under the distributed framework, the proposed algorithm employs CSO-PCG independently to optimize the area dispatch in parallel. The total optimal cost is achieved by minimizing the cost of each area with no centralized controller required. The experimental results on multi-area static economic dispatch (MASED) and multi-area dynamic economic dispatch (MADED) problems show that the proposed DDCSO-PCG algorithm can not only provide a distributed solution, but also achieve the best economic cost compared with other state-of-the-art techniques.

Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization

Accurate electricity price forecasts is the common concern of market participants. With the integration of high penetration of wind and solar energy resources into the power system, the renewable energy sources will have a great impact on the electricity price volatility undoubtedly. In this regard, a novel attention mechanism (AM) based electricity price forecasting model for electricity market with high proportion of renewable energy is proposed in this paper. In order to investigate the effect of renewable energy on the electricity price prediction, the wind power generation, solar power generation, predicted load and the historical price series are simultaneously taken as the input features. In the data preprocessing stage, the empirical wavelet transform (EWT) is applied to decompose each of the input features into multiple components to avoid learning the autocorrelation of the original sequence. In the model training stage, a hybrid AM-based long short-term memory network (LSTM) is proposed as the forecasting model, aiming to make full use of the AM to dynamically evaluate the importance of different input feature. Furthermore, the crisscross optimization algorithm (CSO) is adopted to retrain the parameters of fully-connected layer so as to further enhance the generalization ability. The proposed method is validated on the datasets of Danish electricity market with a high proportion of renewable energy, and the experimental results show that the proposed model is superior to other hybrid models involved in this study.

An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems

As an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defects, a modified whale optimization algorithm integrated with a crisscross optimization algorithm (MWOA-CS) is proposed. In MWOA-CS, each dimension of the optimization problem updates its position by randomly performing improved WOA or crisscross optimization algorithm during the entire iterative process. The improved WOA adopts the new nonlinear convergence factor and nonlinear inertia weight to tune the ability of exploitation and exploration. To analyze the performance of MWOA-CS, a series of numerical experiments were performed on 30 test benchmark functions with dimension ranging from 300 to 1000. The experimental results revealed that the presented MWOA-CS provided better convergence speed and accuracy, and meanwhile, displayed a significantly more effective and robust performance than the original WOA and other state of the art meta-heuristic algorithms for solving large scale global optimization problems.

An enhanced hybrid arithmetic optimization algorithm for engineering applications

Arithmetic optimization algorithm (AOA) is a newly well-developed meta-heuristic algorithm that is inspired by the distribution behavior of main arithmetic operators in mathematics. Although the original AOA has shown well competitive performance with popular meta-heuristic algorithms, it still faces the issues of insufficient exploitation ability, ease of falling into local optima and low convergence accuracy in large-scale applications. In order to ameliorate these deficiencies, an enhanced hybrid AOA named CSOAOA, integrated with point set strategy, optimal neighborhood learning strategy and crisscross strategy, is developed in this paper. First, a good point set initialization strategy is added to obtain a higher-quality initial population, which improves the convergence speed of the algorithm. Then, the optimal neighborhood learning strategy is adopted to guide the individual’s search behavior and avoid the algorithm falling into the current local optimum, which boosts the search efficiency and calculation accuracy. Finally, by combining AOA with the crisscross optimization algorithm, the exploration and utilization ability of the crisscross algorithm are integrated into the CSOAOA. These strategies collaborate to enhance AOA in accelerating overall performance. The superiority of the proposed CSOAOA is comprehensively verified by comparing with the original AOA, six improved AOA and numerous celebrated and newly developed algorithms on the well-known 23 classical benchmark functions, IEEE Congress on Evolutionary Computation (CEC) 2019 test suite and IEEE CEC 2020 benchmark functions, respectively. Meanwhile, the practicability of CSOAOA is also highlighted by solving eight real-world engineering design problems. Furthermore, the statistical testing of CSOAOA has been conducted to validate its significance. Experimental results and statistical comparisons manifest the superior performance of CSOAOA over the comparison algorithms in terms of precision, convergence rate and solution quality. Therefore, CSOAOA is potentially a powerful and competitive meta-heuristic algorithm for solving complex engineering optimization problems.

A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization

Accurate wind power forecasting is of great significance for power system operation. In this study, a triple-stage multi-step wind power forecasting approach is proposed by applying attention-based deep residual gated recurrent unit (GRU) network combined with ensemble empirical mode decomposition (EEMD) and crisscross optimization algorithm (CSO). In the data processing stage, the EEMD is used to decompose the wind power/speed time series and a bi-attention mechanism (BA) is applied to enhance the sensitivity of model to the important information from both time and feature dimension. In the prediction stage, a series-connected deep learning model called RGRU consisting of the residual network and GRU is proposed as the forecasting model, aiming to make full use of extracting the static and dynamic coupling relationship among the input features. In the retraining-stage, a high-performance CSO algorithm is adopted to further optimize the fully-connected layer of RGRU model so as to improve the generalization ability of the model. The proposed method is validated on a wind farm located in Spain and the experimental results demonstrate that the proposed hybrid model has significant advantage over other state-of-the-art models involved in this study in terms of prediction accuracy and stability.

Decision-making Optimization of Procurement and Sale Strategies for Power Sales Companies Considering Virtual Power Plants

Introduction The electricity sales company faces the fluctuation of electricity price in the spot market of the wholesale side and the uncertainty of the demand power of the retail side. In order to balance the benefits and risks， it is necessary to determine a reasonable proportion of electricity purchase in the medium and long-term market and the spot market and the time-of-use electricity price for consumers. Method The demand price response curve was used to simulate the contesting of electricity sales companies in the medium and long-term contract market， and a quantitative method of transaction risk loss index based on conditional value at risk （CVaR） was proposed. Result The demand response based on virtual power plant is realized through flexible load and distributed power generation， so as to balance the power deviation and the risk of price fluctuation in the spot market. On this basis， a two-level optimization model considering the power purchase strategy of electricity sales companies in the wholesale side market and the revenue of power selling on the retail side is established， and the crisscross optimization algorithm is used for solving the problem. Conclusion The simulation results show the effectiveness of the method.

Reversible data hiding method for multi-histogram point selection based on improved crisscross optimization algorithm

Traditional prediction error expansion (PEE)-based reversible data hiding (RDH) schemes focus on generating a sharp prediction error histogram (PEH) in order to increase embedding performance, but they neglect the influence of local complexity on embedding performance. When the PEH is partitioned into multiple sub-histograms by means of the local complexity, the embedding performance can be increased by excluding the embedding of data in the sub-histograms located in the texture regions. However, multiple sub-histograms also cause a problem about how to identify the optimal embedding points that can achieve the best visual quality for a given payload. Violent iteration is often used in RDH schemes to traverse all possible values of the embedding points in order to identify the optimal points. Therefore, we conclude that violent iteration is a very time-consuming method and that it leads to unacceptable computational complexity. The traditional multiple sub-histogram methods usually reduce the solution space (i.e., all possible combinations of the embedding points) to decrease the computational complexity. However, the solution that is obtained from the aforementioned methods may significantly deviate from the global optimal solution. Instead of shrinking the solution space by abandoning most solutions, we improve the crisscross optimization algorithm in order to search for the optimal solution in the global solution space. In this paper, the K-means clustering algorithm is used to classify all prediction errors into multiple categories according to the local complexity. Each category would generate a PEH. Subsequently, the problem of selecting the embedding points of multiple sub-histograms is transformed into a typical and multi-choice knapsack problem. The improved crisscross optimization algorithm is used to determine the approximate optimal solution. The experimental results showed that our scheme provided effective performance.

The multi-objective optimal allocation method of static var compensator based on transient voltage security control

Based on the detailed consideration of the dynamic characteristics of Static Var Compensator (SVC), the mathematical method for installing location and optimal capacity of SVC considering transient voltage security is proposed. Firstly, the trajectory sensitivity index is used to determine the key fault set and SVC candidate location. Then, transient voltage security constraint of the bus voltage is restored to 0.75pu within 1s and the bus voltage is restored to 0.9pu within 3s after remove fault, Finally, considering the transient voltage recovery characteristics and dynamic characteristics of SVC, the minimum of bus voltage deviation after fault removed and minimum of installtion capacity of SVC as objective function for hybrid optimal allocation model. The bus voltage and capacity of SVC are introduced as variables into the Crisscross Optimization Algorithm (CSO) for solving. The effectiveness of the proposed SVC installation location and capacity optimization method is verified by the time domain simulation of the IEEE39-bus system.

Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs

Short-term hydrothermal generation scheduling (SHGS) considering various hydraulic and electric constraints is a complex non-convex optimization problem. The coupling connection of cascaded reservoirs and the valve-point effects of thermal units greatly increase the difficulty of finding optimal solution. This paper presents an efficient solution to the SHGS problem by using a novel crisscross optimization (CSO) algorithm, which generates the optimal scheduling results by applying two distinctive search operators, i.e., horizontal crossover and vertical crossover. The horizontal crossover is used as a global optimizer that can reduce the search blind spots in complex solution space through a cross-border search strategy. The vertical crossover is used to address the premature convergence problem by applying a unique dimensional crossover mechanism. Both search operators take turns to generate moderation solutions and CSO always maintains a population of historically best solutions by using a greedy strategy so as to speed up the convergence speed. To investigate the CSO’s performance on the SHGS problem, three test systems widely adopted in the literature are used to do the short-term hydrothermal generation scheduling. The results reveal that the proposed algorithm has good robustness and outperforms other state-of-the-art methods in terms of solution quality.

A Hybrid Multi-Objective Crisscross Optimization for Dynamic Economic/Emission Dispatch Considering Plug-In Electric Vehicles Penetration

Due to the significant uncertainty of charging time and charging power consumption, the large increase in plug-in electric vehicles (PEVs) may create a major influences on the power system: According to people’s living habits, PEVs are basically charged during peak load periods (after work). Once PEVs continue the random charging behavior, there will be a higher difference of peak-valley and bigger burden on the grid. A new strategy is put forward for dynamic economic/emission dispatch (DEED) with the consideration of PEVs for the purpose to shave the peak and fill the valley in this paper, and the influences brought from different loads of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) on DEED problem are discussed. The problem to be solved is a challenging multi-objective non-linear problem. By taking advantage of the differential evolution (DE) algorithms and a newly developed crisscross optimization algorithm, a new multi-objective hybrid optimization algorithm is put forward to deal with the problem including effectively dealing with the inequality and equality constraints. A case study is presented to show the feasibility and effectiveness of the put forward method. The analysis results demonstrate that the put forward algorithm could effectively solve DEED problem, showing that the resulting approach of peak shaving and valley filling could significantly save economic costs and reduce emissions under the same load.