Hybrid Equilibrium Research Articles

Exploring the importance of stochasticity to Hybrid Equilibria in a Discrete Signaling Game.

Communication via evolved signals is ubiquitous (both within and between species) in the natural world. However, how honest we should expect signals to be remains an open question. Hybrid equilibria are a form of equilibria predicted by discrete signaling games in which signalers are sometimes dishonest and signals do not completely reliably convey information on signaler quality. While these equilibria have been theoretically demonstrated in several signaling games, their dynamics in a stochastic simulation of evolutionary trajectories (that include representation of the inherent noise expected in evolution in the natural world) have not previously been studied. In this paper, we present an agent-based simulation of a discrete signaling game which exhibits hybrid equilibria. We find that while hybrid equilibria are evolutionarily attractive where they exist, populations exhibit variable and often drastic oscillating behavior around the predicted equilibrium values. We discuss how these dynamics might offer valuable opportunity for detecting hybrid equilibria in natural populations.

Experimental validation of a novel hybrid Equilibrium Slime Mould Optimization for solar photovoltaic system

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT technique is a challenge faced by researchers due to factors such as fluctuations in irradiance and the presence of partial shading. This paper introduced a novel hybrid Equilibrium Slime Mould Optimization (ESMO) MPPT-based algorithm combining the advantages of two recent algorithms, Slime Mould Optimization (SMO) and Equilibrium Optimizer (EO). The ESMO algorithm is compared with highly efficient MPPT-based techniques such as SMO, EO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA), both under a Simulink environment and a real-time experimental laboratory setup using a Dspace1104 controller and PV emulator. The comparison focuses on performance under several irradiance cases, including instant irradiance change, partial shading, complex partial shading, and dynamic partial shading. The key advantage of ESMO is the fact that it has a single tunable parameter, which makes implementation much easier and, at the same time, reduces the computational resources that are required by the control system. Extensive testing proves the superiority of ESMO over all other techniques, the average efficiency of which is 99.98% under all conditions. Additionally, ESMO provides fast average tracking times of 244 ms under simulation experiments and 200 ms for real-time experiments. These results show that ESMO can be very important for future implementation in large-scale solar PV systems.

Self-Quenched Fluorophore-DNA Labels for Super-Resolution Fluorescence Microscopy.

Protein labeling through transient and repetitive hybridization of short, fluorophore-labeled DNA oligonucleotides has become widely applied in various optical super-resolution microscopy methods. The main advantages are multitarget imaging and molecular quantification. A challenge is the high background signal originating from the presence of unbound fluorophore-DNA labels in solution. Here, we report the self-quenching of fluorophore dimers conjugated to DNA oligonucleotides as a general concept to reduce the fluorescence background. Upon hybridization, the fluorescence signals of both fluorophores are restored. We expand the toolbox of fluorophores suitable for self-quenching and report their spectra and hybridization equilibria. We apply self-quenched fluorophore-DNA labels to stimulated emission depletion microscopy and single-molecule localization microscopy and report improved imaging performances.

Hybrid equilibrium formulation with adaptive element side orientation for cohesive crack prediction

AbstractThe present article proposes an hybrid equilibrium element (HEE) formulation for the prediction of cohesive fracture formation and propagation with the crack modelled by extrinsic interface embedded at element sides. The hybrid equilibrium element formulation can model high order (quadratic, cubic and quartic) stress fields which strongly satisfy homogeneous equilibrium equations, inter‐element and boundary equilibrium equations. The HEE can implicitly model both the initially rigid behaviour of an extrinsic interface and its debonding condition with separation displacement and softening. The extrinsic interface is embedded at the element sides and its behaviour is governed by means of the same degrees of freedom of HEE (generalized stresses), without any additional degree of freedom. The proposed extrinsic cohesive model is developed in the thermodynamic framework of damage mechanics. The proposed crack propagation criterion states that crack grows when the maximum principal stress reaches the tensile strength value, in a direction orthogonal to the principal stress direction. The crack is embedded at an element side and the mesh around crack tip is adapted, by rotation of the element sides, in order to have the interface aligned to the crack growth direction. Three classic two‐dimensional problems of fracture propagation are numerically reproduced and the results compared to the experimental data or to the other numerical results.

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

Abstract Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.

An Efficient Binary Hybrid Equilibrium Algorithm for Binary Optimization Problems: Analysis, Validation, and Case Studies

Binary optimization problems belong to the NP-hard class because their solutions are hard to find in a known time. The traditional techniques could not be applied to tackle those problems because the computational cost required by them increases exponentially with increasing the dimensions of the optimization problems. Therefore, over the last few years, researchers have paid attention to the metaheuristic algorithms for tackling those problems in an acceptable time. But unfortunately, those algorithms still suffer from not being able to avert local minima, a lack of population diversity, and low convergence speed. As a result, this paper presents a new binary optimization technique based on integrating the equilibrium optimizer (EO) with a new local search operator, which effectively integrates the single crossover, uniform crossover, mutation operator, flipping operator, and swapping operator to improve its exploration and exploitation operators. In a more general sense, this local search operator is based on two folds: the first fold borrows the single-point crossover and uniform crossover to accelerate the convergence speed, in addition to avoiding falling into local minima using the mutation strategy; the second fold is based on applying two different mutation operators on the best-so-far solution in the hope of finding a better solution: the first operator is the flip mutation operator to flip a bit selected randomly from the given solution, and the second operator is the swap mutation operator to swap two unique positions selected randomly from the given solution. This variant is called a binary hybrid equilibrium optimizer (BHEO) and is applied to three common binary optimization problems: 0–1 knapsack, feature selection, and the Merkle–Hellman knapsack cryptosystem (MHKC) to investigate its effectiveness. The experimental findings of BHEO are compared with those of the classical algorithm and six other well-established evolutionary and swarm-based optimization algorithms. From those findings, it is concluded that BHEO is a strong alternative to tackle binary optimization problems. Quantatively, BHEO could reach an average fitness of 0.090737884 for the feature section problem and an average difference from the optimal profits for some used Knapsack problems of 2.482.

To be green or not to be: How governmental regulation shapes financial institutions' greenwashing behaviors in green finance

Green finance provides important economic support for environmental protection and carbon reduction. However, some financial institutions engage in greenwashing behaviors (GWBs), which raise funds through green finance products (GFPs) without investing in green projects and falsely disclosing environmental information. The GWBs of financial institutions result in tremendous pressure on governmental regulation. From the perspective of information disclosure, under incomplete information, we constructed a signaling game between governments and financial institutions to explore the formation mechanism of financial institutions' GWBs. Subsequently, the separating equilibrium, pooling equilibrium, and hybrid equilibrium are investigated. The corresponding conditions for financial institutions choosing positive information disclosure were derived. We also evaluated the effects of different regulatory measures, including rewards, penalties, standards, and certifications on curbing GWBs. Finally, countermeasures to shape financial institutions to undertake their environmental responsibilities and conduct positive information disclosure are proposed. This study provides a theoretical explanation for GWBs and sheds new light on an effective governance model in green finance.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

A New Optimized FOPIDA-FOIDN Controller for the Frequency Regulation of Hybrid Multi-Area Interconnected Microgrids

This paper proposes a combined feedback and feed-forward control system to support the frequency regulation of multi-area interconnected hybrid microgrids considering renewable energy sources (RESs). The proposed control system is based on a fractional-order proportional-integral-derivative-accelerated (FOPIDA) controller in the feed-forward direction and a fractional-order integral-derivative with a low-pass filter compensator (FOIDN) controller in the feedback direction, referred to as a FOPIDA-FOIDN controller. Moreover, the parameters of the proposed FOPIDA-FOIDN controller (i.e., twelve parameters in each area) are optimally tuned using a proposed hybrid of two metaheuristic optimization algorithms, i.e., hybrid artificial gorilla troops optimizer (AGTO) and equilibrium optimizer (EO), and this hybrid is referred to as HGTOEO. The robustness and reliability of the proposed control system are validated by evaluating its performance in comparison to that of other counterparts’ controllers utilized in the literature, such as PID, FOPID, and tilt integral derivative (TID) controller, under the different operating conditions of the studied system. Furthermore, the proficiency of the proposed HGTOEO algorithm is checked against other powerful optimizers, such as the genetic algorithm, Jaya algorithm, improved Jaya algorithm, multi-verse optimizer, and cost-effective multi-verse optimizer, to optimally design the PID controller for the load frequency control of the studied two-area interconnected microgrid. The MATLAB simulation results demonstrate the viability and dependability of the proposed FOPIDA-FOIDN controller based on the HGTOEO algorithm under a variety of load perturbations and random production of RESs.

An efficient methodology for complementary finite element approximations in three‐dimensional elasticity

AbstractWe present a methodology for the definition of the matrices involved in stress based finite element approximations in three‐dimensional problems. In order to handle the increased complexity of the 3D case, a rotation of the global reference frame is used, which greatly simplifies the expressions involved. This rotation is also applicable to the 2D case. The main focus of this work concerns the matrices used in the implementation of the hybrid equilibrium approach. However, a similar complementary methodology (displacement based) is also presented, allowing for an efficient definition of the matrices required for dual analysis, wherein bounds of the solution error are obtained from pairs of complementary solutions, one compatible and the other equilibrated.

Optimized Non-Integer Load Frequency Control Scheme for Interconnected Microgrids in Remote Areas with High Renewable Energy and Electric Vehicle Penetrations

Renewable energy systems (RESs) have introduced themselves as vital solutions for energy supply in remote regions, wherein main utility supply systems are not available. The construction of microgrid (MG) systems is useful candidate for proper control and management with hybrid RESs. However, RESs-based MGs face reduced power system inertia due to the dependency of RESs on power electronic converter systems. Accordingly, preserving nominal operating frequency and reduced deviations in tie-line power are crucial elements for proper operation of interconnected MGs in remote areas. To overcome this problem, load frequency control (LFC) systems have proven featured solutions. Therefore, this paper proposes a new non-integer LFC method based on the fractional order (FO) control theory for LFC in interconnected MGs in remote areas. The proposed control is based on the three degree of freedom (3DoF) cascaded 1+proportional-integral-derivative-accelerated (PIDA) controller with FOPI controller, namely 3DoF 1+PIDA/FOPI LFC scheme. The proposed 3DoF 1+PIDA/FOPItakes the advantages of the accelerated term of PIDA control to improve power system transients, regarding maximum overshoot/undershoot and settling times. Additionally, it employs outer loop to reduce errors and faster inner loop to mitigate disturbances effects. The contribution of plug-in controlled electric vehicles (EVs) are considered to enhance the frequency regulation functions. An optimized design of the proposed 3DoF 1+PIDA/FOPI LFC scheme is proposed using the newly developed hybrid equilibrium optimizer (EO)-slime mould optimization (SMA) algorithm (namely EOSMA optimizer). The EOSMA combines the features of the EO and SMA powerful optimization algorithms. A two interconnected MGs in remote areas with RESs and EVs inclusions with high penetration levels is selected to verify the proposed 3DoF 1+PIDA/FOPI LFC scheme and the EOSMA optimizer. The results show high ability of the proposed controller and design scheme to minimize MGs’ frequency and tie-line power fluctuations and to preserve frequency stability and security.

OESV-KRF: Optimal ensemble support vector kernel random forest based early detection and classification of skin diseases

The most widespread disease that often puts the patient’s life in danger is skin disease. Avoiding this situation, it necessitates the development of efficient and reliable skin cancer prediction techniques. Besides, regular screening of suspicious lesions in the skin helps the dermatologist to detect skin cancer in the early phase. Although there exist numerous Machine Learning methods to detect skin cancer in its early stage, the accurate and timely prediction and classification of skin lesions continued to be demanding. To fulfill this aim, in this paper, we proposed an ensemble support vector kernel random forest-based hybrid equilibrium Aquila optimization (ESVMKRF-HEAO) approach. The proposed prediction model is examined using the HAM10000 dataset which contains numerous varieties of skin lesion images in it. Initially, the intrusions and noises in the dataset are removed, and image qualities are enhanced using preprocessing pipelines. Then, the thresholding-based segmentation technique is utilized to segment the cancerous lesion regions from the healthy background. Finally, the proposed classifier accurately predicts and classifies the segmented images based on their feature characteristics into five distinct categories namely melanocytic nevus, basal cell carcinoma, melanoma, actinic keratosis and dermatofibroma. The simulation of the proposed model is performed using MATLAB 2019a software. The evaluation of the proposed ESVMKRF-HEAO approach performance is done concerning metrics such as sensitivity, f-1 score, accuracy, precision and specificity. The proposed ESVMKRF-HEAO approach attained a greater performance rate with respect to all metrics especially, the prediction accuracy of about 97.4% in the experimental results.

The Weak Hybrid Equilibria of an Exchange Economy with a Continuum of Agents and Externalities

Abstract Inspired by Zhao, J. 1996. “The Hybrid Equilibria and Core Selection in Exchange Economies with Externalities.” Journal of Mathematical Economics 26 (4): 387–407, Askoura, Y. 2011. “The Weak-Core of a Game in Normal Form with a Continuum of Players.” Journal of Mathematical Economics 47: 43–7, Askoura, Y. 2017. “On the Core of Normal Form Games with a Continuum of Players.” Mathematical Social Sciences 89: 32–42, Yang, Z. 2020. “The Weak α-core of Exchange Economies with a Continuum of Players and Pseudo-utilities.” Journal of Mathematical Economies 91: 43–50 and Yang, Z., and X. Zhang. 2021. “A Weak α-core Existence Theorem of Games with Nonordered Preferences and a Continuum of Agents.” Journal of Mathematical Economics 94: 102464, we establish an exchange economy with externalities and a continuum of agents. We define the weak hybrid equilibrium in this model and prove the existence theorem under the regular conditions. Furthermore, we analyze the relation between the set of hybrid equilibria and the set of competitive equilibria in an exchange economy without externalities and with a continuum of agents.

A binary Bi-phase mutation-based hybrid Equilibrium Optimizer for feature selection in medical datasets classification

With the rapid expansion in Biological Sciences, biomedical data classification has become challenging. These datasets generally consist of missing values, redundant features and irrelevant information. A novel hybrid wrapper-based feature selection method is proposed to tackle these issues effectively. In order to improve the exploration ability of the particles, the Sine factor is integrated with the Equilibrium Optimizer (EO) technique. A Bi-phase Mutation (BM) scheme is integrated to enhance the exploitation phase of the EO algorithm (BM-based Hybrid EO, BMHEO). The BMHEO method is evaluated by employing four different classifiers – KNN, SVM, Random Forest (RF) and Discriminant Analysis (DA). It is observed that the Random Forest classifier exhibits superior performance compared to the other three classifiers. Eight S-shaped and V-shaped transfer functions are integrated to convert the solutions to binary form. Based on the above enhancements, eight different versions of BMHEO are produced. The performance of these versions is assessed using twenty biomedical datasets. Experiments on these datasets demonstrate that the BMHEO-S2 version outperforms other methods in terms of fitness values and classification accuracies. This approach is also tested on a MED-NODE image dataset for classification, and higher accuracy of 97.02% is achieved.

An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data

Automatic clustering and dimension reduction are two of the most intriguing topics in the field of clustering. Affinity propagation (AP) is a representative graph-based clustering algorithm in unsupervised learning. However, extracting features from high-dimensional data and providing satisfactory clustering results is a serious challenge for the AP algorithm. Besides, the clustering performance of the AP algorithm is sensitive to preference. In this paper, an improved affinity propagation based on optimization of preference (APBOP) is proposed for automatic clustering on high-dimensional data. This method is optimized to solve the difficult problem of determining the preference of affinity propagation and the poor clustering effect for non-convex data distribution. First, t-distributed stochastic neighbor embedding is introduced to reduce the dimensionality of the original data to solve the redundancy problem caused by excessively high dimensionality. Second, an improved hybrid equilibrium optimizer based on the crisscross strategy (HEOC) is proposed to optimize preference selection. HEOC introduces the crisscross strategy to enhance local search and convergence efficiency. The benchmark function experiments indicate that the HEOC algorithm has better accuracy and convergence rate than other swarm intelligence algorithms. Simulation experiments on high-dimensional and real-world datasets show that APBOP has better effectiveness.

Novel blade angle controllers techniques based on heuristics algorithms

The raised pollution census and the electricity demand–supply gab are significantly increasing the investments in the wind energy (WE). Thus, the paper is dedicated to the wind farm blade angle controllers’ efficiency. This efficiency is increased by enhancing the blade angle control system performance through optimization algorithms. The paper presents a comprehensive comparison between six optimization algorithm combinations to initiate the adaptive PI controller (API). The Harmony Search Algorithm (HSA) hybrid Equilibrium Optimization (EO) assured its distinction over the other algorithms. Moreover, two new controllers are proposed: modified API and Fractional API. Normal and fault case studies are applied to validate the proposed controllers through comparison with classical controllers. The modified API controller assured its distinction in the normal case studies relying on its second order amplifier block. While the fractional API outperforms the other controllers in the fault case study relying on its integral term fractional order. Also, the paper proposed an experimental investigation of blade angle effects on power extraction.

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator.

In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.

Numerical study of the effect of kinetic damping on resistive wall modes with plasma toroidal rotation in CFETR

Herein, the effect of kinetic damping on the resistive wall mode (RWM) instability in the China nuclear fusion engineering test reactor (CFETR) is numerically studied using the magnetohydrodynamic resistive spectrum-kinetic (MARS-K) code. A CFETR hybrid equilibrium with a plasma current of Ip = 13 MA is adopted. The sum of the electron diamagnetic, plasma rotation, and mode frequencies increases at q = 2 rational surface with increasing plasma rotation frequency. When the sum of the frequencies is less than 0, kinetic damping has a stabilizing effect on RWM. In contrast, when the sum of the frequencies is greater than 0, the kinetic damping has a destabilizing effect on RWM, causing a second unstable branch to appear. Additionally, the effects of plasma pressure and wall distance on the two unstable branches are investigated. The mode frequency is excited with increasing plasma pressure and wall distance. Consequently, the first branch is stabilized and the second branch is destabilized.

Weak convergence theorems for symmetric generalized hybrid mappings and equilibrium problems

<p style='text-indent:20px;'>In this paper, we introduce three new iterative methods for finding a common point of the set of fixed points of a symmetric generalized hybrid mapping and the set of solutions of an equilibrium problem in a real Hilbert space. Each method can be considered as an combination of Ishikawa's process with the proximal point algorithm, the extragradient algorithm with or without linesearch. Under certain conditions on parameters, the iteration sequences generated by the proposed methods are proved to be weakly convergent to a solution of the problem. These results extend the previous results given in the literature. A numerical example is also provided to illustrate the proposed algorithms.</p>

Inter-Element Crack Propagation with High-Order Stress Equilibrium Element

The present contribution proposes a formulation based on the use of hybrid equilibrium elements (HEEs), for the analysis of inter-element delamination and fracture propagation problems. HEEs are defined in terms of quadratic stress fields, which strongly verify both the homogeneous and inter-element equilibrium equations and they are employed with interfaces, initially exhibiting rigid behavior, embedded at the elements’ sides. The interface model is formulated in terms of the same degrees of freedom of the HEE, without any additional burden. The cohesive zone model (CZM) of the extrinsic interface is rigorously developed in the damage mechanics framework, with perfect adhesion at the pre-failure condition and with linear softening at the post-failure regime. After a brief review, the formulation is computationally tested by simulating the behavior of a double-cantilever-beam with diagonal loads; the obtained numerical results confirm the accuracy and potential of the method.