Constrained Evolution Research Articles

An adaptive coordinate systems for constrained differential evolution

An adaptive coordinate systems for constrained differential evolution

Promoters Constrain Evolution of Expression Levels of Essential Genes in Escherichia coli.

Variability in expression levels in response to random genomic mutations varies among genes, influencing both the facilitation and constraint of phenotypic evolution in organisms. Despite its importance, both the underlying mechanisms and evolutionary origins of this variability remain largely unknown due to the mixed contributions of cis- and trans-acting elements. To address this issue, we focused on the mutational variability of cis-acting elements, that is, promoter regions, in Escherichia coli. Random mutations were introduced into the natural and synthetic promoters to generate mutant promoter libraries. By comparing the variance in promoter activity of these mutant libraries, we found no significant difference in mutational variability in promoter activity between promoter groups, suggesting the absence of a signature of natural selection for mutational robustness. In contrast, the promoters controlling essential genes exhibited a remarkable bias in mutational variability, with mutants displaying higher activities than the wild types being relatively rare compared to those with lower activities. Our evolutionary simulation on a rugged fitness landscape provided a rationale for this vulnerability. These findings suggest that past selection created nonuniform mutational variability in promoters biased toward lower activities of random mutants, which now constrains the future evolution of downstream essential genes toward higher expression levels.

An initial value insensitive constrained linear predictive evolution algorithm for gas–liquid phase equilibrium calculation problems

Heuristic algorithms are gradually becoming a type of new promising methods for solving phase equilibrium calculation problems since they do not have to calculate an initial value in advance like traditional methods such as direct Newton's method and indirect thermodynamic method. A new heuristic optimizer, constrained linear prediction evolution algorithm (CLPE) for phase equilibrium calculation under given volume, temperature, and moles (NVT-flash) is proposed in this paper. CLPE employs the total Helmholtz free energy of the NVT-flash problem as its objective function and employs the volume and moles vector of a certain phase as decision variables. Numerical experiments are conducted on four NVT-flash problems. The consistency between the experimental results and those obtained by some traditional methods verifies that the proposed CLPE is effective. The comparative advantage in computational overhead over the similar algorithms indicates the significance of this study. The success of CLPE can drive more heuristic algorithms to solve NVT-flash problems more efficiently, so as to advance the field of phase equilibrium calculation.

A Multiobjective Optimization Algorithm for Fluid Catalytic Cracking Process with Constraints and Dynamic Environments

The optimization problems in a fluid catalytic cracking process with dynamic constraints and conflicting objectives are challenging due to the complicated constraints and dynamic environments. The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise. To solve these problems, we established a mathematical model and proposed a dynamic constrained multiobjective optimization evolution algorithm for the fluid catalytic cracking process. In this algorithm, we design an offspring generation strategy based on minimax solutions, which can explore more feasible regions and converge quickly. Additionally, a dynamic response strategy based on population feasibility is proposed to improve the feasible and infeasible solutions by different perturbations, respectively. To verify the effectiveness of the algorithm, we test the algorithm on ten instances based on the hypervolume metric. Experimental results show that the proposed algorithm is highly competitive with several state-of-the-art competitors.

A constrained differential evolution algorithm for calculation of two-phase equilibria at given volume, temperature and moles

A variety of strategies including two types of direct solution methods based on Newton's method and indirect solution methods based on thermodynamic principles have been developed, which can find satisfactory solutions for the phase equilibrium calculation problem at given volume, temperature and moles (NVT-flash). However, these methods are sensitive to the initial values and depend on the objective function's derivative during calculation. To resolve these deficiencies, a constrained differential evolution algorithm (abbreviated as CDE) for calculating two-phase equilibria at given volume, temperature, and moles is presented in this paper. The total Helmholtz free energy is regarded as the objective function, and the moles vector and volume of a certain phase are taken as the decision variables. This paper deals with the constraints of the NVT-flash problem by using direct search method and exterior point method. The results of the investigations on pure substance, binary and ternary mixtures are in agreement with the published data, which proves the effectiveness in solving the NVT-flash problem and the energy attenuation characteristic of the algorithm. The proposed algorithm represents the first successful attempt to apply the meta-heuristic optimization algorithm to solve the phase equilibrium computation under NVT-flash, and it shows the promising application prospect of meta-heuristic optimization algorithms for solving the phase equilibrium calculation problems.

Constrained multiobjective optimization design for ordinary shovel attachment of hydraulic excavator based on evolutionary algorithm

Shovel attachment is one of the most commonly used working attachments in hydraulic excavators. Its mechanical design remains a challenging optimization problem. To tackle this issue, the optimizing model of ordinary shovel attachment is firstly established which contains twenty-three design variables, six objective functions, and sixty-three constraints. Then, an improved decomposition-based constrained many-objective evolution algorithm is proposed, which integrates advanced push & pull search constraint handling technique and differential evolution operator to deal with the optimization problems of shovel attachment. Finally, the proposed algorithm has demonstrated its superior optimization ability for designing shovel attachments through the case studies of a 70-ton face-shovel hydraulic excavator, outperforming seventeen state-of-the-art constrained multiobjective evolutionary algorithms. Furthermore, the first quantitative comparison between ordinary shovel attachment and TriPower shovel attachment under the same main machine is presented by the optimized results. The result demonstrates the complexity of the optimal design for shovel attachment and the effectiveness of multiobjective evolutionary algorithm.

Digest: Pelagic habitats promote speciation but constrain morphological evolution.

What happens when ecological factors predicted to promote evolutionary change coincide with factors predicted to constrain it? A recent study by Burress and Hart (2024) shows that the transition of North American minnows from a competitive and complex benthic habitat to a noncompetitive and homogeneous pelagic habitat is associated with an increase in speciation rates but a decrease in rates of morphological evolution. These results demonstrate that different dimensions of evolutionary change may respond to different ecological factors.

A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

Constrained evolution of Hamiltonian phase space distributions in the presence of natural, non-conservative forces

Confidence regions for spacecraft state can be constructed in phase space which encapsulate some region where there is a likelihood for the state to reside. These regions can be treated as phase space distributions or structures. Structures, such as surfaces or volumes, are constrained to preserve specific properties as they evolve in phase space under Hamiltonian dynamics. Thus, spacecraft uncertainty is then constrained by Hamiltonian flow which can provide insight into state determination. This work examines the modified constraints in the presence of non-conservative forces which relate to both probabilistic and geometric properties of the evolving uncertainty structure. The modified constraints are then derived for a Two-Body and drag environment and are shown to be valid after comparison with alternative methods. Applying the modified constraints, the constrained evolution of the confidence region is then tied to a simple physical explanation for the changing knowledge in our spacecraft state, in the atmospheric drag environment and Poynting–Robertson drag environment.

Joint Interdependent Task Scheduling and Energy Balancing for Multi-UAV-Enabled Aerial Edge Computing: A Multiobjective Optimization Approach

To provide a dependency-aware application, multiple UAVs are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we focus on the joint interdependent task scheduling and energy balancing for MU-AEC by using a multi-objective optimization approach, which enables a decision maker to identify the optimal solutions corresponding to the best feasible tradeoffs between the two objectives. A constrained multi-objective optimization problem involving two objectives, i.e., the makespan minimization of all tasks and energy balancing among different UAVs, is formulated. In the solution methodology, we propose a constrained decomposition-based multi-objective evolution algorithm. To quickly seek more superior solutions, a local search mechanism by utilizing the objective information, and an improved genetic operator are proposed for remarkable performance improvements. Finally, numerical results demonstrate that compared with the baseline algorithms, our algorithm achieves both advantages in increasing the convergence and diversity of the solutions.

Constrained evolution of effective equation of state parameter in non-linear $$\qquad\qquad f\left( {R,L_{m} } \right)$$ dark energy model: insights from Bayesian analysis of cosmic chronometers and Pantheon samples

Constrained evolution of effective equation of state parameter in non-linear $$\qquad\qquad f\left( {R,L_{m} } \right)$$ dark energy model: insights from Bayesian analysis of cosmic chronometers and Pantheon samples

A constrained multiobjective differential evolution algorithm based on the fusion of two rankings

The tradeoff between objective functions and constraints is a key issue that needs to be addressed by constrained multiobjective optimization algorithms, and constraint handling techniques (CHTs) are an important technique for balancing objective functions and constraints. In this paper, a novel CHT that fuses two rankings is proposed. Specifically, each individual is assigned two rankings: one ranking calculated based on Pareto dominance (regardless of constraints) and another calculated based on the constrained dominance principle (CDP). The fitness value of an individual is the weighted sum of these two rankings, and the weight is related to the generation number and the proportion of feasible solutions in the current generation. Based on the proposed CHT, a constrained multiobjective differential evolution algorithm is proposed. To generate high-quality offspring, the proposed constrained multiobjective differential evolution algorithm combines four mutation operations as core components of the search algorithm. The proposed algorithm is compared with eight state-of-the-art algorithms in experiments with five test suites, and the experimental results show that the proposed algorithm performs significantly better than the eight state-of-the-art algorithms.

Surrogate-assisted optimization for anti-ship missile body configuration considering high-velocity water touching

As a crucial weapon in the sea battle, anti-ship missiles generally employ a sea-skimming penetration strategy to reduce the probability of being detected by the target radar, which greatly increases the risk of touching water caused by sensor errors or random sea conditions. To alleviate the large impact load by high-velocity water touching, a novel anti-ship missile body configuration is proposed in this paper, which is inspired by the idea of hydroplaning. A parametric geometry model is first developed to modify the configuration of the anti-ship missile body. Subsequently, a structured arbitrary Lagrange-Eulerian based Fluid-Structure Interaction (FSI) model is established to analyze the kinematics parameters of the missile body during the hydroplaning process. A missile body configuration optimization problem is then formulated to minimize the impact load considering several constraints, e.g., horizontal velocity loss, pitch angle after touching water, and inside capacity for payload. Due to the time-consuming FSI simulation, a Kriging-assisted constrained differential evolution method is utilized to optimize the missile body configuration for reducing the impact load. During the optimization process, radial basis function and Kriging are combined with evolutionary operators to lead the search to the vicinity of the optimum rapidly. The result shows that the proposed missile body configuration can reduce the impact load by 18.8% compared with the ordinary configuration. Additionally, the optimized configuration can further yield a 17.4% impact load decrease subject to all the constraints and avoid structural damage by the high-velocity water touching, which demonstrates the effectiveness and practicability of the proposed anti-ship missile body configuration and corresponding optimization framework for reducing the impact load.

Robot Dynamics Modeling with a Novel Friction Model and Extracted Feasible Parameters Using Constrained Differential Evolution

Robot Dynamics Modeling with a Novel Friction Model and Extracted Feasible Parameters Using Constrained Differential Evolution

Magma Source and Petrogenesis of the Early Cretaceous Granites in The Liaodong Peninsula: Evidence from In Situ Apatite Sr-Nd and Zircon Hf-O Isotopes

Apatite Sr-Nd and zircon Hf-O isotopes are broadly used to trace magma sources and constrain magma evolution processes, further improving our understanding of the origin of granitoids. We present zircon U-Pb ages, whole-rock major and trace elements, and whole-rock Sr-Nd-Hf, zircon Hf-O, and apatite Sr-Nd isotopic data for the coarse-grained quartz monzonite, biotite monzogranite, and granite porphyry in the Yushulinzi pluton in the Liaodong Peninsula, the eastern North China Craton, to establish their magma sources and petrogenesis. The coarse-grained quartz monzonite, biotite monzogranite, and granite porphyry were formed contemporaneously, with zircon U-Pb ages of 123–119 Ma. They share enriched whole-rock Sr-Nd-Hf and zircon Hf isotopic compositions, and the coarse-grained quartz monzonite has crust-like δ18O values (5.7–6.7‰). The coarse-grained quartz monzonite and biotite monzogranite have variable apatite (87Sr/86Sr)i ratios and negative apatite εNd(t) values. These isotopic characteristics indicate that the different rock types in the Yushulinzi pluton were derived from the partial melting of ancient crustal material in the North China Craton. Their geochemical and petrographic characteristics indicate that the crystal-melt segregation model can be employed to elucidate the genetic links among different rock types, with the coarse-grained quartz monzonite representing crystal accumulation and the biotite monzogranite and granite porphyry representing interstitial melts extracted from a crystal-rich magma chamber. Furthermore, the variable apatite Sr isotopic compositions and subtle differences in the peak zircon εHf(t) values of the studied rock samples confirm the possibility of a contribution from shallow crustal components and materials with high εHf(t) values during magma evolution, which is not readily revealed by their whole-rock Sr-Nd-Hf isotopic compositions. These results demonstrate that in situ apatite Sr-Nd and zircon Hf-O isotopic analyses have the potential to provide distinctive insights into the magma sources and evolution of magmatic systems.

Dexterous workspace optimization for a six degree-of-freedom parallel manipulator based on surrogate-assisted constrained differential evolution

Parallel manipulators are increasingly applied because of their high stiffness, load capacity, and motion precision. However, the workspace of a parallel manipulator is relatively small compared to that of a serial manipulator. Therefore, appropriate structural designs of parallel manipulators for maximizing the workspace are essential. This paper studies the dexterous workspace maximization problem of a six-degree-of-freedom (DOF) parallel manipulator. First, a constrained optimization model is formulated, in which, the objective is to maximize the dexterous workspace, and the constraints include several kinematic and dynamic indicators, and some general structural limitations. To address the highly constrained, computationally-expensive problem, a surrogate-assisted two-phase constrained differential evolution method is proposed. Its effectiveness and efficiency are verified by the superior performance over two surrogate-free evolutionary methods: differential evolution (DE) and genetic algorithm (GA), and two surrogate-assisted state-of-the-art evolutionary methods: modified constrained expected improvement (CEI) and the lower confidence bounding approach (PCLCB), on ten benchmark functions and the established real-word 6-DOF manipulator design optimization problem.

Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution

The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems. Effective constrained optimization methods can be considered in tackling the optimal power flow problems. In this paper, an ϵ-constrained method-based adaptive differential evolution is proposed to solve the optimal power flow problems. The ϵ-constrained method is improved to tackle the constraints, and a p-best selection method based on the constraint violation is implemented in the adaptive differential evolution. The single and multi-objective optimal power flow problems on the IEEE 30-bus test system are used to verify the effectiveness of the proposed and improved εadaptive differential evolution algorithm. The comparison between state-of-the-art algorithms illustrate the effectiveness of the proposed and improved εadaptive differential evolution algorithm. The proposed algorithm demonstrates improvements in nine out of ten cases.

Constrained shuffled complex evolution algorithm and its application in the automatic calibration of Xinanjiang model

The Shuffled Complex Evolution—University of Arizona (SCE-UA) is a classical algorithm in the field of hydrology and water resources, but it cannot solve constrained optimization problems directly. Using penalty functions has been the preferred method to handle constraints, but the appropriate selection of penalty parameters and penalty functions can be challenging. To enhance the universality of the SCE-UA, we propose the Constrained Shuffled Complex Evolution Algorithm (CSCE) to conveniently and effectively solve inequality-constrained optimization problems. Its performance is compared with the SCE-UA using the adaptive penalty function (SCEA) on 14 test problems with inequality constraints. It is further compared with seven other algorithms on two test problems with low success rates. To demonstrate its effect in hydrologic model calibration, the CSCE is applied to the parameter optimization of the Xinanjiang (XAJ) model under synthetic data and observed data. The results indicate that the CSCE is more advantageous than the SCEA in terms of the success rate, stability, feasible rate, and convergence speed. It can guarantee the feasibility of the solution and avoid the problem of deep soil tension water capacity (WDM)<0 in the optimization process of the XAJ model. In the case of synthetic data, the CSCE can accurately find the theoretical optimal parameters of the XAJ model under the given constraints. In the case of observed data, the XAJ model optimized by the CSCE can effectively simulate the hourly rainfall-runoff events of the Hexi Basin and achieves mean Nash efficiency coefficients greater than 0.75 in the calibration period and the validation period.

Developing the optimization control of pollution constrained by the convection diffusion equations

In this paper, an optimization control of pollution subject to the convection diffusion equations is developed and analyzed. The optimization objective function is to consider the pollution effects for minimizing the weighted derivation between simulated concentrations and allowable concentrations at observation sites and the costs of emission reductions, pollution taxes, and the cost related to layout of pipelines. The proposed optimal control problem is defined on the multiple observation and polluting points and is constrained by the convection diffusion equations. We analyze this optimization problem from a rigorous mathematical viewpoint and prove the existence of a solution of the pollution optimal control. We build the numerical optimal control approach by a second-order improved splitting upwind finite volume method to solve the convection diffusion equations and by the constrained difference evolution (DE) algorithm to compute the optimization procedure. Three numerical experiments are investigated, where the first one aims at searching the optimal disposal fluxes while minimizing concentration deviation and reduction cost, the other two are to minimize the whole economical costs that contain waste water treatment cost, pipeline layout cost and pollution taxation by finding the optimal effluent concentration of waste water. The developed optimal control is efficient and it can be applied in applications.

High‐resolution 2.5D multifocusing imaging of a crooked seismic profile in a crystalline rock environment: Results from the Larder Lake area, Ontario, Canada

ABSTRACTA high‐resolution seismic reflection transect was acquired over a hard‐rock geological setting along an existing roadway in the Larder Lake area of the Superior Craton of Canada for the Metal Earth project in 2017. This profile, as well as other Metal earth transects, primarily aims to enhance the knowledge and to better understand the subsurface geology of the Abitibi Greenstone Belt within the Canadian Shield. The complex geological settings of the study area as well as the tribulations caused by the survey geometry have made the imaging and velocity field estimation more challenging. A recently introduced 2.5D multifocusing stacking method is one potential solution for processing crooked‐line seismic data with a poor signal‐to‐noise ratio. The 2.5D multifocusing approach offers more realistic modelling of the zero‐offset wavefield by explicitly accounting for the midpoint dispersion and cross‐dip effects. The main practical problem of the 2.5D multifocusing implementation is the simultaneous determination of the optimal wavefield parameters for each image point and time location. We address this optimization problem using a multidimensional constrained differential evolution global optimization algorithm, as this improves the efficiency and accuracy of the estimation. We have also designed an efficient processing sequence for multifocusing seismic imaging. The performance of the 2.5D multifocusing procedure has been examined on a synthetic model, generated using the same real acquisition geometry. Numerical tests demonstrate that the 2.5D multifocusing technique can produce a more focused stack with the primary reflections appearing at their poststack correct locations, and the procedure can also provide reliable estimates of interface dips. Due to the importance and difficulty of imaging the data, several conventional and advanced processing strategies have been attempted on the transect, specifically: 2D phase‐shift time migration of a dip moveout corrected stack; 2D prestack Kirchhoff time migration; swath 3D poststack migration; and our 2.5D multifocusing imaging algorithm. We found that applying the 2.5D multifocusing stacking algorithm followed by a poststack time migration approach improved the resolution of the image significantly compared to all the conventional and advanced methods and identified new reflections. The 2.5D multifocusing method also focused the steeply dipping reflections more coherently, which resolved ambiguities in geological architecture by understanding the location and continuity of structures. The method also accurately extracts 3D structural information and results in an improved signal‐to‐noise ratio.