Mutation Strategy Research Articles

Optimal Layout of Urban Green Space System Based on the Coupling Model of Spatial Reconstruction

In the layout of urban planning, the rational design of green land includes the optimal choice of landscape and vegetation, which needs to solve the conflict of local/global optimal solution and understand the rationality of urban green layout with multi-objective natural language. According to the demand for multi-objective optimal solutions in urban green land planning, we study the target deep learning algorithm based on space vector mutation operation. In this paper, we propose to search for a local optimal solution based on Diversity Indicator Double and Niched Local Search Evolutionary Algorithm diversity fitness and filter the information of objective space and decision space through the niche method. It intends to avoid the imbalance between the diversity of goal space and decision space caused by the wrong distance information. By improving genetic diversity, we take the complex changes of gene vector reaction in generating offspring as the research focus. Based on the improvement of multi-objective non-dominated genetic variation, we search for the optimal transfer function relationship in the primary stage of variation to achieve the goal. For high-dimensional planning with constraints, we first select the coupling model to reduce the dimension, and then combine the spatial reconstruction data clustering to complete the clustering. Priority is given to the optimal selection of the output of the non-dominated solution set obtained in each stage to improve the algorithm’s performance. Experiments show that the proposed algorithm, combined with the use of different mutation strategies, outperforms the current mainstream algorithms in solving multi-objective problems of natural language understanding, especially in solving complex relational problems, which is 10.2% and 15.6% higher than other algorithms. The frequency of parameter operation can be updated in real-time, which strengthens the convergence effect of the algorithm. It is proved that the research content of this paper has better application potential in multi-objective spatial decision-making. The research content of this paper can not only achieve intelligent layout optimization for urban green space system but also carry out reasoning for large-scale spatial layout. The effect is higher than the current mainstream algorithm and plays a role in promoting science and technology for future urban construction and long-term planning.

Improved active vibration control of a cantilever beam using MFC actuators with Hammerstein model-based hysteresis modeling and VSS-FxLMS control algorithm

This study aims to enhance active vibration control in a cantilever beam using macro fiber composite (MFC) actuators. To address the challenge of accurately modeling the complex hysteresis behavior of MFC actuators, an advanced hysteresis modeling technique is employed, integrating a non-symmetric Bouc–Wen model with an Auto-Regressive model with eXogenous inputs (ARX). The parameter estimation of this model is optimized using an adaptive mutation factor, and multiple mutation strategy differential evolution (AmFMMDE) algorithm. Additionally, a Variable Step Size Filtered-x Least Mean Square (VSS-FxLMS) algorithm with step size adaptation based on normalized error change is utilized for effective vibration control. This approach shows promising potential for practical engineering applications requiring precise and efficient active vibration control.

Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis

This research seeks to promote the field via the design and implementation optimized robotic manipulator control systems, recognizing control techniques' vital role in current engineering applications. This study introduces an improved particle swarm optimization (IPSO) technique that maximizes the efficiency of a Fractional Order Proportional-Integral-Derivative (FOPID) controller by optimally adjusting FOPID gains in robotic manipulator systems. The controller has undergone refinement and enhancement using state-of-the-art particle swarm optimization (PSO) techniques incorporating a cost function and a representative bio-inspired algorithm. The IPSO algorithm enhances global search efficiency by preventing premature convergence and local minima trapping through chaos-based initialization and adaptive mutation strategies. The performance of IPSO-tuned FOPID controllers is benchmarked against conventional PSO-tuned FOPID controllers using various objective functions. The stabilizing fractional order PID controllers demonstrated a higher stability margin than traditional PID controllers. Numerical simulations support the developed strategy by analyzing the step and sinusoidal responses of the closed-loop system within the stability region. The results indicate that IPSO outperforms PSO with improvements of approximately 50% for 10 iterations, about 12% for 50 iterations, and around 20% for 100 iterations across ITSE, ITAE, and ITAE metrics, respectively. Furthermore the statistical analysis based on Wilcoxon sign rank test proof that the IPSO algorithm significantly improves convergence speed, controller accuracy, and overall performance, thereby enhancing the effectiveness of the IPSO technique such as in case of 10 iteration the confidence intervals do not include zeros, which indicates that IPSO outperformed the traditional POS in all scenarios.

Inversion Analysis for Thermal Parameters of Mass Concrete Based on the Sparrow Search Algorithm Improved by Mixed Strategies

In the traditional mass concrete temperature field calculation, the accuracy of the thermal parameters is extremely important. However, the actual thermal parameters of mass concrete may have some errors with the laboratory-measured values or specification values due to the site ambient temperature, concrete surface insulation measures, cooling water flow, etc. Therefore, it can be combined with the measured temperature of the field temperature sensors using the sparrow search algorithm (SSA) for the inverse analysis of thermal parameters. Firstly, to address the problem that SSA has low convergence accuracy and easily falls into local optimum, a mixed strategy was adopted to improve the algorithm, including Logistic Chaos mapping initialization of the population, the introduction of adaptive weighting factors, and the use of the Cauchy mutation strategy. Then, the performance test was carried out to compare the performance of the algorithm with three different intelligent algorithms and reflect the superiority of the SSA that was improved by mixed strategies (SSAIMSs). Finally, the proposed method was applied to the thermal parameter inversion of a mass concrete pile cap. The inversion results demonstrated that SSAIMSs can improve the accuracy and speed of thermal parameter inversion, and the calculated results of the thermal parameters and temperatures obtained using the SSAIMSs matched well with the measured results in the field, which can meet the accuracy requirements of the actual engineering.

Multi-strategy fusion improved Northern Goshawk optimizer is used for engineering problems and UAV path planning

Addressing the imbalance between exploration and exploitation, slow convergence, local optima Traps, and low convergence precision in the Northern Goshawk Optimizer (NGO): Introducing a Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). In response to challenges faced by the Northern Goshawk Optimizer (NGO), including issues like the imbalance between exploration and exploitation, slow convergence, susceptibility to local optima, and low convergence precision, this paper introduces an enhanced variant known as the Multi-Strategy Integrated Northern Goshawk Optimizer (MINGO). The algorithm tackles the balance between exploration and exploitation by improving exploration strategies and development approaches. It incorporates Levy flight strategies to preserve population diversity and enhance convergence precision. Additionally, to avoid getting trapped in local optima, the algorithm introduces Cauchy mutation strategies, improving its capability to escape local optima during the search process. Finally, individuals with poor fitness are eliminated using the crossover strategy of the Differential Evolution algorithm to enhance the overall population quality. To assess the performance of MINGO, this paper conducts an analysis from four perspectives: population diversity, balance between exploration and exploitation, convergence behavior, and various strategy variants. Furthermore, MINGO undergoes testing on the CEC-2017 and CEC-2022 benchmark problems. The test results, along with the Wilcoxon rank-sum test results, demonstrate that MINGO outperforms NGO and other advanced optimization algorithms in terms of overall performance. Finally, the applicability and superiority of MINGO are further validated on six real-world engineering problems and a 3D Trajectory planning for UAVs.

Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO

Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

Differential evolution with adaptive mutation and crossover strategies for nonlinear regression problems

Differential evolution with adaptive mutation and crossover strategies for nonlinear regression problems

An adapted Black Widow Optimization Algorithm for Financial Portfolio Optimization Problem with cardinalty and budget constraints

Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz’s 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version. New features are introduced for the BWAPO to adapt better to the problem, including (1) mating attraction and (2) differential evolution mutation strategy. The proposed BWAPO is evaluated against other metaheuristic approaches used in portfolio optimization from literature, and its performance demonstrates its effectiveness through comparative studies on benchmark datasets using multiple performance metrics, particularly in the unconstrained Mean-Variance portfolio optimization version. Additionally, when encountering cardinality constraint, the proposed approach yields competitive results, especially noticeable with smaller datasets. This leads to a focused examination of the outcomes arising from equality versus inequality cardinality constraints, intending to determine which constraint type is more effective in producing portfolios with higher returns. The paper also presents a comprehensive mathematical model that integrates real-world constraints such as transaction costs, transaction lots, and a dollar-denominated budget, in addition to cardinality and bounding constraints. The model assesses both equality/inequality cardinality constraint versions of the problem, revealing that the inequality constraint tends to offer a wider range of feasible solutions with increased return potential.

Delineation of global, absolutely essential and conditionally essential pangenomes of Porphyromonas gingivalis

Porphyromonas gingivalis is a Gram-negative, anaerobic oral pathobiont, an etiological agent of periodontitis and the most commonly studied periodontal bacterium. Multiple low passage clinical isolates were sequenced, and their genomes compared to several laboratory strains. Phylogenetic distances were mapped, a gene absence-presence matrix generated, and core (present in all genomes) and accessory (absent in one or more genomes) genes delineated. Subsequently, a second pangenome delineating the prevalence of inherently essential genes was generated. The prevalence of genes conditionally essential for surviving tobacco exposure, abscess formation and epithelial invasion was also determined, in addition to genes encoding key proteolytic enzymes containing putative signal peptides. While the absolutely essential pangenome was highly conserved, significant differences in the complete and conditionally essential pangenomes were apparent. Thus, genetic plasticity appears to lie primarily in gene sets facilitating adaptation to variant disease-related environments. Those genes that are highly pervasive in the P. gingivalis absolutely essential pangenome or are highly prevalent and essential for fitness in disease-relevant models, may represent particularly attractive therapeutic targets worthy of further investigation. As mutations in absolutely essential genes are expected to be lethal, the data provided herein should also facilitate improved planning for P. gingivalis gene mutation strategies.

A nonlinear randomly reuse-based mutated whale optimization algorithm and its application for solving engineering problems

The whale optimization algorithm (WOA) is a meta-heuristic optimization algorithm inspired by the social behavior of whale hunting. However, the WOA has the defects of easily falling into local optimum and slow convergence speed. Therefore, to overcome the shortcomings of the original WOA, a novel variant of WOA called nonlinear randomly reuse-based mutated whale optimization algorithm (NRRMWOA) is proposed in this study. In detail, the proposed NRRMWOA includes three novel strategies as compared with original WOA. Firstly, a nonlinear adaptive parameter strategy is introduced to achieve nonlinear adjustment of search pattern along with the iteration time. The second improvement is the random reuse strategy, which can fully utilize the current optimal whale to improve the solution accuracy. The thirdly proposed late disturbance mutation strategy has the function of increasing population diversity in the later stage of iteration. The three proposed strategies complement each other and significantly improve the convergence efficiency and global exploration capability of WOA. To verify the superiority of the proposed NRRMWOA, a series of typical benchmark functions are utilized to conduct comprehensive numerical experiments. Compared with three sets of comparison algorithms (five variants of WOA, five advanced algorithms, and five well-known algorithms), the ARVs (average ranking values) obtained by NRRMWOA are 1.4667, 1.9167, and 2.1333, respectively. The rankings are all the best in the corresponding group. The detailed experimental and statistics results indicate that the presented NRRMWOA has higher convergence speed and better solution quality compared with the three sets of existing algorithms. In addition, 15 real-world optimization problems are employed to further test the performance of NRRMWOA for solving engineering optimization problems. For the results of 15 real-world optimization problems, the proposed NRRMWOA achieves the best results including 8 best “Mean”, 10 best “Min.”, and 7 best “Max.”. As a conclusion, the proposed NRRMWOA is a promising and excellent algorithm.

Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R2) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting.

A Multi-Strategy Improved Northern Goshawk Optimization Algorithm for Optimizing Engineering Problems.

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO's exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO.

SENGraph: A Self-Learning Evolutionary and Node-Aware Graph Network for Soft Sensing in Industrial Processes.

The last decade has witnessed the growing prevalence of deep models on soft sensing in industrial processes. However, most of the existing soft sensing models are developed to learn from regular data in the Euclidean space, ignoring the complex coupling relations among process variables. On the other hand, graph networks are gaining attraction in handling non-Euclidean relations in industrial data. However, the existing graph networks on soft sensing models still suffer from two major issues: 1) how to capture the intervariable structural relations and intravariable temporal dependencies from dynamic and strongly coupled industrial data and 2) how to learn from nodes with distinctive importance for the soft sensing task. To address these problems, we propose a self-learning evolutionary and node-aware graph network (SENGraph) for industrial soft sensing. We first develop a self-learning graph generation (SLG) module to combine the coarse-and fine-grained graphs to capture the global trend and local dynamics from process data. Then, we build a self-evolutionary graph module (EGM) to obtain diversified node features from the entire graph using mutation and crossover strategies. Finally, we design a node-aware module (NAM) to highlight the informative nodes and suppress the less significant ones to further improve the discriminative ability of the downstream soft sensing. Extensive experimental results and analysis on four real-world industrial datasets demonstrate that our proposed SENGraph model outperforms the existing state-of-the-art (SOTA) soft sensing methods.

A hybrid method for fault diagnosis of rolling bearings

Abstract Traditional diagnostic methods often have insufficient accuracy and noise reduction, which leads to diagnostic errors. To address these issues, this paper proposes an advanced fault diagnosis model that combines the variational mode decomposition (VMD) improved by a Variable-Objective Search Whale Optimization Algorithm (VSWOA) with a Pelican Optimization (PO)-boosted Kernel Extreme Learning Machine (KELM) algorithm. The application of the method is shown here in the fault diagnosis of rolling bearings. The proposed VSWOA enhances the performance of VMD by incorporating a Sobol sequence, nonlinear time-varying factors, a multi-objective initial search strategy, and an elite Cauchy chaos mutation strategy, significantly improving noise reduction in vibration signals. Fault information is precisely extracted using waveform factors, sample entropy, and advanced composite multiscale fuzzy entropy, which enables effective feature screening and dimensionality reduction. The POA fine-tunes the KELM parameters, increasing the classification accuracy. The effectiveness of the model is verified through experimental evaluations using bearing data with injected Gaussian noise (from Case Western Reserve University) and the SpectraQuest datasets, where significant improvements in noise reduction and fault detection accuracy are achieved.

Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem

Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

An Adaptive Differential Evolution Algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints

An Adaptive Differential Evolution Algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints

Intelligent Method for Mutation of Input Cases with Feedback

Relevance. Fuzzing is one of the effective ways to improve the software reliability and is included in the mandatory list of research carried out at the stage of qualification testing according to national standard GOST R 56939-2016. The use of standard mutators reduces the fuzzing process to brute force, which negatively affects the time of incorrect program behavior detection. In this regard, it is important to rationalize the selection of input data, which takes into account the data corpus specifics, as well as the context describing the software response under test and allowing to determine the mutations at the next iteration of testing. Purpose of the research is to increase the efficiency of fuzzing by intellectualizing the standard mutator using neural networks, which takes into account the syntactic and semantic features of the input corpus and uses program feedback.Methods. The methods of analysis and synthesis, theory of algorithms, discrete and computational mathematics, machine learning were used.Result. The advantages and disadvantages of the standard module for AFL fuzzer’s input corpus mutation are considered. The justification of neural network choice with LSTM-architecture as a mechanism that realizes the intelligent control of input corpora’s’ generation and transformation is given. The proposed mutation method is described, which implies the integration of decision making mechanism on the amount and format of necessary mutations to increase the code coverage into the standard mutator, as well as the subsequent refinement of input data by shell-code to check the operability of the fragment that caused abnormal software’s behavior. The scheme of the mutation module is presented, which includes a component of input corporas conversion for generation of program execution traces and a component aimed at concept confirmation and re-call of abnormal software behavior using the generated shell-code.Novelty. Unlike the known ones, the proposed method uses feedback, fixing the software reaction, when forming the data mutation strategy, which determines the scientific novelty of the obtained results.Significance. The proposed solution allows reducing the program testing time while maintaining the code coverage. The results obtained in the research are universal and, in the future, can be used in white, black and gray box fuzzing methods.

An enhanced snow ablation optimizer for UAV swarm path planning and engineering design problems

The Snow Ablation Optimizer (SAO) is an advanced optimization algorithm. However, it suffers from slow convergence and a tendency to become trapped in local optima. To address these limitations, we propose an Enhanced Snow Ablation Optimization algorithm (ESAO). Initially, an adaptive T-distribution control strategy is employed to improve the algorithm's exploratory position adjustments, facilitating the identification of the global optimum. Furthermore, we introduce a Cauchy mutation strategy, endowing individuals with a robust capability to escape local extrema and steer the population towards more favorable directions. A leader-based boundary control strategy is also proposed to enhance the optimizer's search performance, significantly increasing the accuracy, speed, and stability of the algorithm in tackling complex problems. To validate the performance of ESAO, we utilize 29 CEC2017 benchmark functions for comparison against eight popular algorithms across various dimensions. Our algorithm ranked first in all comparisons, demonstrating ESAO's effectiveness. Additionally, to evaluate the practical applicability of the proposed method, we mathematically modeled the UAV swarm and solved the UAV swarm path planning problem using various competitor algorithms. Furthermore, we applied different competitor algorithms to two engineering design problems. The results demonstrate that ESAO performs the best. In general, ESAO outperforms its counterparts in terms of solution quality and stability, showcasing its superior application potential.

AdaGuiDE: An adaptive and guided differential evolution for continuous optimization problems

Differential evolution (DE) has been proven as a simple yet powerful meta-heuristic algorithm on tackling continuous optimization problems. Nevertheless most existing DE methods still suffer from certain drawbacks including the use of ineffective mechanisms to adjust mutation strategies and their control parameters that may possibly mislead the search directions, and also the lack of intelligent guidance and reset mechanisms to escape from local optima. Therefore, to enhance the adaptability of DE-based search frameworks and the robustness on optimizing complex problems full of local optima, an adaptive and guided differential evolution (AdaGuiDE) algorithm is proposed. Essentially, the adaptability of the AdaGuiDE search framework is enhanced by three schemes to iteratively refine the search behaviour at two different levels. At the macroscopic level, the AdaGuiDE search framework revises the existing adaptive mechanism for selecting appropriate DE search strategies by counting the actual contributions in terms of solution quality. In addition, the adaption strategy is extended to the microscopic level where a penalty-based guided DE search is employed to guide the search escaping from local optima through temporarily penalizing the local optima and their neighborhood. Furthermore, a systematic boundary revision scheme is introduced to dynamically adjust the search boundary for locating any potential regions of interest during the search. For a rigorous evaluation of the proposed search framework, the AdaGuiDE algorithm is compared against other well-known meta-heuristic approaches on three sets of benchmark functions involving different dimensions in which the AdaGuiDE algorithm attained remarkable results especially on the high-dimensional and complex optimization problems. More importantly, the proposed AdaGuiDE framework shed lights on many possible directions to further enhance the adaptability of the underlying DE-based search strategies in tackling many challenging real-world applications.

A novel hybrid adaptive differential evolution for global optimization

Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.