Diffusion models have achieved remarkable success in image generation, image super-resolution, and text-to-image synthesis. Despite their effectiveness, they face key challenges, notably long inference time and complex architectures that incur high computational costs. While various methods have been proposed to reduce inference steps and accelerate computation, the optimization of diffusion model architectures has received comparatively limited attention. To address this gap, we propose LDMOES (Lightweight Diffusion Models based on Multi-Objective Evolutionary Search), a framework that combines multi-objective evolutionary neural architecture search with knowledge distillation to design efficient UNet-based diffusion models. By adopting a modular search space, LDMOES effectively decouples architecture components for improved search efficiency. We validated our method on multiple datasets, including CIFAR-10, Tiny-ImageNet, CelebA-HQ [Formula: see text], and LSUN-church [Formula: see text]. Experiments show that LDMOES reduces multiply-accumulate operations (MACs) by approximately 40% in pixel space while outperforming the teacher model. When transferred to the larger-scale Tiny-ImageNet dataset, it still generates high-quality images with a competitive FID score of 4.16, demonstrating strong generalization ability. In latent space, MACs are reduced by about 50% with negligible performance loss. After transferring to the more complex LSUN-church dataset, the model surpasses baselines in generation quality while reducing computational cost by nearly 60%, validating the effectiveness and transferability of the multi-objective search strategy. Code and models will be available at https://github.com/GenerativeMind-arch/LDMOES .

ABSTRACT This paper introduces an innovative Light‐Weight Consensus model for Context‐Sensitive Side Chains (abbreviated as LWC2S2C) to enhance the efficiency of consensus mechanisms within blockchain networks. The proposed model adopts a multifaceted approach, taking into account many intricate parameters including the number of blocks within the sidechain, the architecture of each block, the performance metrics of individual miners, and the complicated interplay between source nodes and miner nodes. These various metrics are combined to give each miner a unique rank, which affects their chances of being chosen for the important block validation process. The consequential discoveries derived from this comprehensive analysis reveal that the LWC2S2C model eclipses its contemporary counterparts in consensus mechanisms. Through the meticulous examination of performance benchmarks, including mining delay, energy consumption, and throughput, within diverse application scenarios, namely Electronic Health Records (EHR), Internet of Medical Things (IoMT), and Enterprise Resource Planning (ERP), the LWC2S2C consistently manifests an enviable supremacy. Remarkably, it outpaces the Practical Byzantine Fault Tolerance (PBFT) by an impressive 10.5%, surpasses the Proof of Practicality and Trust (PoPT) by a striking 14.6%, and leaves the Improved Proof of Trust (IPoT) behind by an astonishing 18.4% in the context of mining delay. Furthermore, in the case of energy efficiency, the LWC2S2C model consumes 6.2% less energy compared to PBFT, 10.5% less energy compared to PoPT, and a substantial 16.8% less energy when contrasted with IPoT. These findings emphatically underscore the scalability, efficiency, and low energy footprint that the LWC2S2C model brings to the fore. The obtained results demonstrate that LWC2S2C reaches highly awarding inference tailored to the exigencies of sidechain‐based applications.

In this work, we present a novel method of increasing the entropy of the CNT-PUF, a Physical Unclonable Function (PUF) based on Carbon-NanoTube Field Effect Transistors (CNT-FETs). The binary responses of this PUF are based on the drain current IDof each CNT-FET under the influence of a particular gate-source voltage VGS,which, through the employment of a single threshold value for ID,can indicate whether each relevant CNT cell of the array is conducting (acting either as a true conductor or as a semiconductor) or not (acting as an insulator). In this work, we propose the adoption of individual threshold values for each such cell as part of the relevant PUF challenge, thereby significantly increasing the overall entropy of this PUF, as well as the security that it can provide. Moreover, this method allows for the realisation of a source of higher entropy in the form of a True Random Number Generator (TRNG). Finally, we note that our work and its results are most probably also relevant for other CNT- based PUFs, structures, and primitives that utilise a single current (or even, voltage) threshold to determine the state of the different CNT cells utilised.

Establishing an accurate equivalent model is a critical foundation to describe the energy conversion characteristics of a photovoltaic system, which can support the research of fault analysis, output power prediction, and performance analysis of the photovoltaic system. However, the widely used equivalent models are highly nonlinear and have many unknown parameters, making it difficult to identify these parameters accurately. Our previous work found that the gaining‐sharing knowledge‐based algorithm (GSK) shows promising performance in solving this problem. But its efficacy is not enough to achieve accurate parameters within a relatively limited computing resource. In this context, a dual‐population GSK algorithm (DPGSK), which introduces a dual‐population evolution strategy for more excellent searchability, is proposed to address this issue. In each iteration, the population splits equally and randomly into two subpopulations, one of which performs the junior gaining‐sharing phase while the other performs the senior gaining‐sharing phase. Then two updated subpopulations merge to form a new population. This allows for a grand reconciliation of convergence speed and population diversity, giving DPGSK powerful optimization performance. Afterward, DPGSK is applied to five photovoltaic models and validated for performance against other advanced metaheuristics. Besides, the impact of different components on DPGSK is also investigated. Results and comparisons show that either component is indispensable to DPGSK, and DPGSK strengthens the convergence and achieves accurate and reliable results, demonstrating its superiority over other algorithms in solving this studied problem.

Abstract To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the most challenging task. In this paper, performance of the various developed meta-heuristic algorithms are evaluated on the recently developed CEC 2021 benchmark functions. The objective functions are parametrized by inclusion of the operators, such as bias, shift and rotation. The different combinations of the binary operators are applied to the objective functions which leads to the CEC2021 benchmark functions. Therefore, different meta-heuristic algorithms are considered which solve the benchmark functions with different dimensions. The performance of some basic, advanced meta-heuristics algorithms and the algorithms that participated in the CEC2021 competition have been experimentally investigated and many observations, recommendations, conclusions have been reached. The experimental results show the performance of meta-heuristic algorithms on the different combinations of binary parameterized operators.

The success of differential evolution algorithm depends on its offspring breeding strategy and the associated control parameters. Improved Multi-Operator Differential Evolution (IMODE) proved its efficiency and ranked first in the CEC2020 competition. In this paper, an improved IMODE, called IMODEII, is introduced. In IMODEII, Reinforcement Learning (RL), a computational methodology that simulates interaction-based learning, is used as an adaptive operator selection approach. RL is used to select the best-performing action among three of them in the optimization process to evolve a set of solution based on the population state and reward value. Different from IMODE, only two mutation strategies have been used in IMODEII. We tested the performance of the proposed IMODEII by considering 12 benchmark functions with 10 and 20 variables taken from CEC2022 competition on single objective bound constrained numerical optimisation. A comparison between the proposed IMODEII and the state-of-the-art algorithms is conducted, with the results demonstrating the efficiency of the proposed IMODEII.

The rapid growth of economic makes the process of manufacturing become a political, social, and community concerns. The manufacture selection is a complex multi-criteria decision making (MCDM) issue. The achievement of optimum alternative with respect to manufacture criteria have various diverse procedures. However, recognizing suitable MCDM methods to be adequate for manufacture process is critical for the success to achieve the ideal manufacture. The selection of ideal manufacture decisions is taken in conditions of uncertainty that difficult handled by the traditional methods. Therefore, a proposed hybrid methodology of neutrosophic theory with several MCDM techniques of Analytic Hierarchy Process (AHP), Multi-Objective Optimization based on Ratio Analysis (MULTIMOORA), Multi-Attributive Border Approximation Area Comparison (MABAC), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to the purposes of manufacture selection. The assessment factors of computational complexity, adequacy to changes of criteria, and agility are applied on the proposed MCDM methods to evaluate sufficiency of the decision process. An empirical study is presented to illustrate the suitability and applicability for suggested methodology. The outcomes demonstrated that proposed hybrid methodology is convenient for manufacture selection. A comparative study is applied, and the results showed, the agility in decision process, AHP technique executed well than MABAC, MULTIMOORA and TOPSIS. The computational complexity, the MABAC technique executed well than AHP, MULTIMOORA and TOPSIS. Additionally, MABAC, MULTIMOORA and TOPSIS techniques are recommended to be the choice of the manufacture selection for adequacy to changes of criteria. Consequently, the comparative study contributes to decision makers and researchers to select the most proper methodology for the process of manufacture selection

Nowadays, firms are trying to execute and use blockchain technology (BT) for rising the products and service goodness in the supply chain (SC). Based on the specific requirements, the BT can be used in several areas. The BT assets various segments of Supply Chain Management (SCM) with consideration of several effective features that are characterized to be multi-criteria decision making (MCDM) issues with environmental restrictions of uncertainty conditions. This research illustrates the suitability of BT in SCM for various segments that are assessed using a neutrosophic model according to single-valued neutrosophic sets (SVNSs). Also, contributes as an evaluation model that combines neutrosophic set, with MCDM methods of Analytic Hierarchy Process (AHP), VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). The proposed study uses the AHP method to generate weights of criteria considering the expert's perspectives. Moreover, uses the neutrosophic theory to handle uncertain situations. The alternatives ranked based on outcomes of applying TOPSIS and VIKOR methods. A case study presents a hierarchical MCDM issue of 7 criteria and 20 sub-criteria with alternative segments assessed. As a result, the medicine segment is recommended to be the best alternative according to the proposed methods of AHP, TOPSIS, and VIKOR while the insurance segment is not recommended by AHP and TOPSIS methods and jewelry segments are not recommended in the VIKOR method.

Fault section diagnosis (FSD) is a critical part of the power system dispatching and control. To diagnose the faulty section(s) correctly, an improved binary variant of gaining-sharing knowledge-based algorithm (GSK) named LOBGSK is presented in this paper. It stretches the original GSK over binary search space so as to solve the 0-1 integer programming FSD problem. In LOBGSK, individuals are encoded by binary numbers and logical operations instead of real arithmetic operations are designed to update the individuals. By this, LOBGSK can avoid transcoding in solving the FSD problem. To validate the effectiveness of LOBGSK, it is first applied to a 4-substation test system considering various fault scenarios. Then it is further implemented to the IEEE 118-bus system and an actual fault event occurred in a practical power grid in Jilin province of China. In addition, the influence of three key parameters of LOBGSK is also investigated. Simulation results show that LOBGSK is robust against its key parameters and can offer a 100% successful rate to diagnose different faults quickly, which is demonstrated by the reported results of some published FSD methods. Furthermore, it outperforms seven state-of-the-art metaheuristic algorithms and the original GSK in solving the FSD problem of power systems.

Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon’s test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs.

The initiative to introduce new benchmark problems has drawn attention to the development of new optimization algorithms. Recently, a set of constrained benchmark problems has been developed as a addition to CEC benchmark series. This paper proposed a hybrid variant of gaining sharing knowledge based algorithm with adaptive parameters and improved multi-operator differential evolution (IMODE) algorithm, called APGSK-IMODE. It enhanced the performance of recently developed adaptive gaining sharing knowledge based algorithm. The performance of APGSK-IMODE has been tested on CEC2021 benchmark problems which contains 10 test functions with dimensions 10 and 20. The results obtained from the proposed algorithm have been compared with those obtained from the rival algorithms. The results elaborate the superiority of APGSK-IMODE. APGSK-IMODE outperforms the competing algorithms with regard to quality of solution, robustness and convergence.

The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. The objective of the optimization problem is to obtain a feasible solution that minimizes the total power consumption of the WSN, when the error probability at the fusion center meets certain criteria. This work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing to the power allocation of decentralized detection. More specifically, we apply and compare four algorithms designed to tackle Large scale global optimization (LSGO) problems. These are the memetic linear population size reduction and semi-parameter adaptation (MLSHADE-SPA), the contribution-based cooperative coevolution recursive differential grouping (CBCC-RDG3), the differential grouping with spectral clustering-differential evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive differential evolution (EADE). To the best of the authors knowledge, this is the first time that LSGO algorithms are applied to the optimal power allocation problem in IoT networks. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.

This paper introduces an enhancement of the recent developed Gaining Sharing Knowledge based algorithm, dubbed as GSK. This algorithm is an excellent example of a contemporary nature-based algorithm which is inspired from the human life behavior of gaining and sharing knowledge to solve the optimization task. GSK algorithm simulates the natural phenomena of human gaining and sharing knowledge using two main phases: junior and senior. A set of initial solutions are generated at the beginning of the search which are consideredjuniors. Later, the individuals are moving to senior stage by interacting with the environment and cooperating with other solutions during the search. The key idea in this work is to extend and improve the original GSK algorithm by proposing adaptive settings to the two important control parameters: knowledge factor and knowledge ratio. These two parameters are responsible to control junior and senior gaining and sharing phases between the solutions during the optimization loop. The algorithm is named AGSK and tested on the recent benchmark suite on bound constrained numerical optimization which consists of different challenging optimization problems with different dimensions. This benchmark is presented in IEEE-CEC2020 competition. When compared with other state-of-the-art algorithms including original GSK, AGSK shows superior performance.

To improve the optimization performance of LSHADE algorithm, an alternative adaptation approach for the selection of control parameters is proposed. The proposed algorithm, named LSHADE-SPA, uses a new semi-parameter adaptation approach to effectively adapt the values of the scaling factor of the Differential evolution algorithm. The proposed approach consists of two different settings for two control parameters F and Cr. The benefit of this approach is to prove that the semi-adaptive algorithm is better than pure random algorithm or fully adaptive or self-adaptive algorithm. To enhance the performance of our algorithm, we also introduced a hybridization framework named LSHADE-SPACMA between LSHADE-SPA and a modified version of CMA-ES. The modified version of CMA-ES undergoes the crossover operation to improve the exploration capability of the proposed framework. In LSHADE-SPACMA both algorithms will work simultaneously on the same population, but more populations will be assigned gradually to the better performance algorithm. In order to verify and analyze the performance of both LSHADE-SPA and LSHADE-SPACMA, Numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions, including a comparison with LSHADE algorithm are executed. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, of both LSHADE-SPA and LSHADE-SPACMA are better than LSHADE algorithm, especially as the dimension increases.