Promising Regions Research Articles

A novel multi-objective evolutionary algorithm with a two-fold constraint-handling mechanism for multiple UAV path planning

Most multiple unmanned aerial vehicle (UAV) path planning problems are often treated as constrained single-objective optimization problems. How to consider them as constrained multi-objective optimization problems (CMOPs) have seldom been explored in this field. To fill this gap, this paper firstly constructs multiple UAV path planning problem as a CMOP with two objectives and five constraints. Then a novel multi-objective evolutionary algorithm with a two-fold constraint-handling mechanism is proposed for multiple UAV path planning. To cope with constraints effectively, a constraint-handling technique based on a progressive weight vector strategy is proposed. Besides, a constraint repair technique that considers the flying environment is designed to further guide the algorithm to find feasible promising regions. Eight multiple UAV path planning test instances with different solving difficulties are constructed. Subsequently, they are used to validate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is superior over four compared algorithms in terms of obtaining a set of better-distributed Pareto optimal solutions.

A Surrogate-Assisted Evolutionary Framework With Regions of Interests-Based Data Selection for Expensive Constrained Optimization

Optimization problems whose evaluations of the objective and constraints involve costly numerical simulations or physical experiments are referred to as expensive constrained optimization (ECO) problems. Such problems can be solved by evolutionary algorithms (EAs) in conjunction with computationally cheap surrogates that separately approximate the expensive objective and constraint functions. During the process of the ECO, the interested regions of surrogate models for the objective and constraints usually have a small overlap only. Specifically, the surrogate model for the objective function should focus on the prediction accuracy in the promising region, while the models for constraint functions should concentrate on the accuracy at the boundary of the feasible region. However, most existing methods neglect such differences and train those different models using the same training data, barely resulting in satisfactory performance. Therefore, we propose a general framework for solving expensive optimization problems with inequality constraints. In the proposed framework, the objective and constraints are separately trained with two different sets of training data to enhance the prediction accuracy and reliability in the interested regions. A novel infill sampling criterion is tailored to decide whether potentially better or more uncertain solutions should be sampled. Moreover, a new strategy, termed search intensity adjustment, is designed for adjusting the number of search generations on new surrogate models. We attempt to embed three competitive constrained EAs into our framework to verify its generality. The experimental results obtained on numerous benchmark functions from CEC2006, CEC2010, and CEC2017 have demonstrated the superiority of our approach over three state-of-the-art surrogate-assisted EAs.

Quantitative trait locus mapping of osmotic stress response in the fungal wheat pathogen Zymoseptoria tritici.

Osmotic stress is a ubiquitous and potent stress for all living organisms, but few studies have investigated the genetic basis of salt tolerance in filamentous fungi. The main aim of this study was to identify regions of the genome associated with tolerance to potassium chloride (KCl) in the wheat pathogen Zymoseptoria tritici. A secondary aim was to identify candidate genes affecting salt tolerance within the most promising chromosomal regions. We achieved these aims with a quantitative trait locus (QTL) mapping study using offspring from 2 crosses grown in vitro in the presence or absence of osmotic stress imposed by 0.75 M KCl. We identified significant QTL for most of the traits in both crosses. Several QTLs overlapped with QTL identified in earlier studies for other traits, and some QTL explained trait variation in both the control and salt stress environments. A significant QTL on chromosome 3 explained variation in colony radius at 8-day postinoculation (dpi) in the KCl environment as well as colony radius KCl tolerance at 8 dpi. The QTL peak had a high logarithm of the odds ratio (LOD) and encompassed an interval containing only 36 genes. Six of these genes present promising candidates for functional analyses. A gene ontology (GO) enrichment analysis of QTL unique to the KCl environment found evidence for the enrichment of functions involved in osmotic stress responses.

Quadratic Interpolation Optimization (QIO): A new optimization algorithm based on generalized quadratic interpolation and its applications to real-world engineering problems

An original math-inspired meta-heuristic algorithm, named quadratic interpolation optimization (QIO), is proposed to address numerical optimization and engineering issues. The main inspiration behind QIO is derived from mathematics, specifically the newly proposed generalized quadratic interpolation (GQI) method. This method overcomes the limitations of the traditional quadratic interpolation method to better find the minimizer of the quadratic function formed by any three points. The QIO utilizes the GQI method as a promising searching mechanism for tackling various types of optimization problems. This searching mechanism delivers exploration and exploitation strategies, in which the minimizer provided by the GQI method assists the QIO algorithm in exploring a promising region in unexplored areas and exploit the optimal solutions in promising regions. To evaluate QIO’s effectiveness, it is comprehensively compared with 12 other commonly used optimizers on 23 benchmark test functions and the CEC-2014 test suite. Ten engineering problems are also tested to assess QIO’s practicality. Eventually, a real-world application of QIO is presented in the operation management of a microgrid with an energy storage system. The results demonstrate that QIO is a promising alternative for addressing practical challenges. The source code of QIO is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/135627-quadratic-interpolation-optimization-qio.

INTERNATIONAL MARKETING STRATEGY OF THE COMPANY IN THE CONTEXT OF GLOBALISATION

In this scientific article, the importance of developing an international marketing strategy for companies in the context of globalization of the business environment and internationalization is considered. The article focuses on the processes of analyzing the markets of potential countries, selecting the most promising regions or countries, developing commercial offers for goods, services, and prices, as well as establishing plans for distribution and communication policy in international markets. The importance of in-depth market analysis and taking into account their characteristics in the formation of an international marketing strategy is emphasized, which should be aimed at achieving success in international markets by changing cognitive preferences through the use of smart marketing tools and an appropriate approach to marketing activities. This approach is defined as critical for the sustainable growth and prosperity of companies in international markets in the modern conditions of the global economy and competition. It has been proven that the successful operation of the enterprise on the global market is achieved due to a properly formed international marketing strategy. The purpose of the research is the analysis of theoretical aspects and the development of practical recommendations for the formation of effective marketing strategies in the conditions of globalization. The essence of the international marketing strategy, which should be developed and implemented when the enterprise enters foreign markets, is determined. It is noted that in the conditions of globalization, enterprises choose access to the global market and choose an international marketing strategy. For this purpose, an algorithm for forming a marketing strategy for an enterprise entering the international market is proposed, which includes the following stages: determination of the goals and possible tasks of the enterprise entering the international market, analysis of the international marketing environment, analysis of the resource base of the enterprise, decision on the feasibility of entering the foreign market, selection market, choosing a method of entering the foreign market, developing a market strategy, developing a product strategy, developing mechanisms for implementing the chosen international marketing strategy.

Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning

In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm’s solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications.

Abnormal individualized peak functional connectivity toward potential repetitive transcranial magnetic stimulation treatment of autism spectrum disorder.

Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging has been widely applied to guide precise repetitive transcranial magnetic stimulation (rTMS). The left, right, and bilateral dorsolateral prefrontal cortices (DLPFC) have been used as rTMS treatment target regions for autism spectrum disorder (ASD), albeit with moderate efficacy. Thus, we aimed to develop an individualized localization method for rTMS treatment of ASD. We included 266 male ASDs and 297 male typically-developed controls (TDCs) from the Autism Brain Imaging Data Exchange Dataset. The nucleus accumbens (NAc) was regarded as a promising effective region, which was used as a seed and individualized peak FC strength in the DLPFC was compared between ASD and TDC. Correlation analysis was conducted between individualized peak FC strength and symptoms in ASD. We also investigated the spatial distribution of individualized peak FC locations in the DLPFC and conducted voxel-wise analysis to compare NAc-based FC between the two groups. ASD showed stronger peak FC in the right DLPFC related to TDC (Cohen's d = -.19, 95% CI: -0.36 to -0.03, t = -2.30, p = .02). Moreover, negative correlation was found between the peak FC strength in the right DLPFC and Autism Diagnostic Observation Schedule (ADOS) scores, which assessed both the social communication and interaction (r = -.147, p = .04, uncorrected significant), and stereotyped behaviors and restricted interests (r = -.198, p = .02, corrected significant). Peak FC locations varied substantially across participants. No significant differences in NAc-based FC in the DLPFC were found in the voxel-wise comparison. Our study supports the use of individualized peak FC-guided precise rTMS treatment of male ASD. Moreover, stimulating the right DLPFC might alleviate core symptoms of ASD.

Quantitative characterization of tight gas sandstone reservoirs using seismic data via an integrated rock-physics-based framework

Seismic characterizing of tight gas sandstone (TGS) reservoirs is essential for identifying promising gas-bearing regions. However, exploring the petrophysical significance of seismic-inverted elastic properties is challenging due to the complex microstructures in TGSs. Meanwhile, interbedded structures of sandstone and mudstone intensify the difficulty in accurately extracting the crucial tight sandstone properties. An integrated rock-physics-based framework is proposed to estimate the reservoir quality of TGSs from seismic data. TGSs with complex pore structures are modeled using the double-porosity model, providing a practical tool to compute rock physics templates for reservoir parameter estimation. The VP/VS ratio is utilized to predict the cumulative thickness of the TGS reservoirs within the target range via the threshold value evaluated from wireline logs for lithology discrimination. This approach also facilitates better capturing the elastic properties of the TGSs for quantitative seismic interpretation. Total porosity is estimated from P-wave impedance using the correlation obtained based on wireline log analysis. After that, the three-dimensional rock-physics templates integrated with the estimated total porosity are constructed to interpret microfracture porosity and gas saturation from velocity ratio and bulk modulus. The integrated framework can optimally estimate the parameters dominating the reservoir quality. The results of the indicator proposed based on the obtained parameters are in good agreement with the gas productions and can be utilized to predict promising TGS reservoirs. Moreover, the results suggest that considering microfracture porosity allows a more accurate prediction of high-quality reservoirs, further validating the applicability of the proposed method in the studied region.

Numerical simulation of heat and mass transfer in the Hurghada–El Gouna geothermal field in Egypt

Egypt's current power generation capacity relies primarily on oil and natural gas. The Egyptian government's strategic objective of alternative energy source development is under severe strain due to the country's growing energy needs. Successive Egyptian governments have prioritized increasing renewable energy production as part of the country's overall energy output, with geothermal energy included as one of the new sources in Egypt's renewable energy program. Previous studies have highlighted the suitability of areas around the Red Sea and the Gulf of Suez for geothermal energy exploration; among these, the Hurghada and El Gouna area, located south of the Gulf of Suez at the Sinai Peninsula's triple junction with Africa and Arabia, is considered the most promising geothermal resource region. This study's main objective is to develop conceptual and numerical models of the Hurghada–El Gouna geothermal area based on temperature data obtained from deep onshore and offshore oil and gas wells, in addition to existing geological and structural information. Our numerical simulations reveal the presence of geothermal resources in the area, which are attributed to high, fault-controlled heat flow and deep subsurface water circulation. The rate of motion of water increases as it moves vertically through the fractures. After analyzing the simulation results, we assessed the feasibility of geothermal energy generation. Our findings indicate that the studied region has the potential to produce approximately 75 MW of electricity over a 25-year lifespan and 37.5 MW over a 50-year lifespan.

An improved rough set strategy-based sine cosine algorithm for engineering optimization problems

In this paper, a hybrid algorithm called rough sine cosine algorithm (RSCA) is introduced for solving engineering optimization problems by merging the sine cosine algorithm (SCA) with the rough set theory concepts (RST). RSCA combines the benefits of SCA and RST to focus the search for a promising region where the global solution can be found. Due to imprecise information on the optimization problems, efficient algorithms roughly identify the optimal solution for this type of uncertain data. The fundamental motive for adding the RST is to deal with the imprecision and roughness of the available information regarding the global optimal, especially for large dimensional problems. The cut concept of RST targeted the more interesting search region so the optimal operation could be sped up, and the global optimum could be reached at a low computational cost. The proposed RSCA algorithm is tested on 23 benchmark functions and 3 design problems. RSCA’s obtained results are mainly compared to the SCA, which is used as a first level of the proposed algorithm in this work and those of other algorithms in the literature. According to the comparisons, the RSCA can provide very competitive performance with different algorithms.

Research Activity as a Management Factor Innovative Development of the Far East

Modern economic thought inextricably links economic growth with the development and implementation of innovations. At the moment, in Russia, due to the ongoing foreign policy events, the problem of managing innovative development has acquired the greatest urgency. The basic factor of managing the innovative development of the country and regions is research activity. One of the most promising regions for its development is the Far Eastern Federal District of the Russian Federation. In this regard, the purpose of the study is a comprehensive assessment of the state and effectiveness of research activities as a factor in managing the innovative development of the Far Eastern Federal District, determining its strengths and weaknesses and potential for growth as a driver of innovative development of the region. The research methodology is based on the theoretical analysis of the main scientific provisions related to the management of the national innovation system, as well as research activities as the main factor of its development. The statistical data on the state of research activities in Russia, the Far Eastern Federal District and other federal districts, as well as rating assessments, served as an informational and empirical basis for the study. The scientific novelty of the study consists in a comprehensive assessment based on the selected criteria of the state and role of research activity as a factor in managing the innovative development of the Far Eastern Federal District, determining its strengths and weaknesses and possible potential for growth. The study showed a significant lag in the research activity of the Far East from the average Russian values, which indicates its low level of development. The current contribution of the Far Eastern Federal District to the most important indicators of innovative development of the federal districts of the Russian Federation is quite small, which indicates the insignificant role of the region in the innovative development of the country. At the same time, the innovative development of the Far Eastern Federal District and the state of its research activities have a high potential for growth, which can be realized through the planned management of such advanced regional innovation centers emerging in the region as Khabarovsk Krai, Primorsky Krai and Sakhalin Oblast.

Dual Elite Groups-Guided Differential Evolution for Global Numerical Optimization

Differential evolution (DE) has shown remarkable performance in solving continuous optimization problems. However, its optimization performance still encounters limitations when confronted with complex optimization problems with lots of local regions. To address this issue, this paper proposes a dual elite groups-guided mutation strategy called “DE/current-to-duelite/1” for DE. As a result, a novel DE variant called DEGGDE is developed. Instead of only using the elites in the current population to direct the evolution of all individuals, DEGGDE additionally maintains an archive to store the obsolete parent individuals and then assembles the elites in both the current population and the archive to guide the mutation of all individuals. In this way, the diversity of the guiding exemplars in the mutation is expectedly promoted. With the guidance of these diverse elites, a good balance between exploration of the complex search space and exploitation of the found promising regions is hopefully maintained in DEGGDE. As a result, DEGGDE expectedly achieves good optimization performance in solving complex optimization problems. A large number of experiments are conducted on the CEC’2017 benchmark set with three different dimension sizes to demonstrate the effectiveness of DEGGDE. Experimental results have confirmed that DEGGDE performs competitively with or even significantly better than eleven state-of-the-art and representative DE variants.

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.

Adaptive Discretization using Voronoi Trees for Continuous POMDPs

Solving continuous Partially Observable Markov Decision Processes (POMDPs) is challenging, particularly for high-dimensional continuous action spaces. To alleviate this difficulty, we propose a new sampling-based online POMDP solver, called A daptive D iscretization using V oronoi T rees (ADVT). It uses Monte Carlo Tree Search in combination with an adaptive discretization of the action space as well as optimistic optimization to efficiently sample high-dimensional continuous action spaces and compute the best action to perform. Specifically, we adaptively discretize the action space for each sampled belief using a hierarchical partition called Voronoi tree, which is a Binary Space Partitioning that implicitly maintains the partition of a cell as the Voronoi diagram of two points sampled from the cell. ADVT uses the estimated diameters of the cells to form an upper-confidence bound on the action value function within the cell, guiding the Monte Carlo Tree Search expansion and further discretization of the action space. This enables ADVT to better exploit local information with respect to the action value function, allowing faster identification of the most promising regions in the action space, compared to existing solvers. Voronoi trees keep the cost of partitioning and estimating the diameter of each cell low, even in high-dimensional spaces where many sampled points are required to cover the space well. ADVT additionally handles continuous observation spaces, by adopting an observation progressive widening strategy, along with a weighted particle representation of beliefs. Experimental results indicate that ADVT scales substantially better to high-dimensional continuous action spaces, compared to state-of-the-art methods.

Solving Highly Expensive Optimization Problems via Evolutionary Expected Improvement

Although many methods have been proposed to solve expensive optimization problems (EOPs), they often consume hundreds of function evaluations (FEs) to find the optimal solution, which is unacceptable when facing highly EOPs. To reduce the number of FEs, we incorporate the population distribution into the well-known expected improvement (EI); thus, a new infill criterion called evolutionary EI (EEI) is proposed. In EEI, the covariance matrix adaptation evolution strategy is used to provide the population distribution. Compared with the original EI, EEI focuses more on promising regions provided by the population distribution, thus, reducing the FEs wasted in unpromising regions. By employing EEI as the infill criterion of Bayesian optimization, a new algorithm called EEI-BO is designed. Moreover, we also introduce an extended version of EEI-BO, called EEI-BO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> , to handle multitask EOPs. To verify the effectiveness of EEI-BO, it is used to solve 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> , 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> , and 30-D test problems by using only 40, 50, and 60 FEs, respectively. The results show that EEI-BO is able to obtain high-quality solutions by consuming limited FEs. In addition, we apply EEI-BO to deal with the lightweight and crashworthiness design of the side body of an automobile. The results demonstrate that EEI-BO performs well on solving it. Furthermore, the performance of EEI-BO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{+}$</tex-math> </inline-formula> is investigated by nine test problems. The results show that it has the capability to solve multitask EOPs with fast convergence speed.

The Directed Multi-Objective Estimation Distribution Algorithm (D-MOEDA)

Improvement Direction Mapping (IDM) methods have been applied as a local search strategy to hybridize global search algorithms. A natural question is whether this concept could be applied within a global search scheme, so that the stochastic search operators are directed toward promising regions, promoting a more efficient search. This paper introduces a novel Multi-Objective Evolutionary Algorithm (MOEA) that incorporates the IDM into the reproduction operator of an Estimation of Distribution Algorithm (EDA). In this proposal, the search directions of the IDM based on aggregation functions are used to directly steer the search process of a multi-objective evolutionary algorithm based on decomposition, by orienting a local probability distribution towards a search direction, the proposal intends to steer solutions toward the Pareto front (PF) of the Multi-Objective Optimization Problem (MOP), exploiting the search features of the aggregation functions. The proposal is tested using a set of well-known benchmark MOPs and compared to state of the art MOEAs. Results showed statistical evidence about the importance of the orientation of the search probability distribution to improve the convergence to the Pareto front.

Relevant Region sampling strategy with adaptive heuristic for asymptotically optimal path planning

Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the most promising regions, which significantly reduces planning time-consumption. The RRT# algorithm defines the Relevant Region based on the cost-to-come provided by the optimal forward-searching tree. However, it uses the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. To improve the path planning efficiency, we propose a batch sampling method that samples in a refined Relevant Region with a direct sampling strategy, which is defined according to the optimal cost-to-come and the adaptive cost-to-go, taking advantage of various sources of heuristic information. The proposed sampling approach allows the algorithm to build the search tree in the direction of the most promising area, resulting in a superior initial solution quality and reducing the overall computation time compared to related work. To validate the effectiveness of our method, we conducted several simulations in both SE(2) and SE(3) state spaces. And the simulation results demonstrate the superiorities of proposed algorithm.

Hybrid particle swarm optimization algorithms for cost-oriented robotic assembly line balancing problems

Purpose The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I. Design/methodology/approach Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed. Findings A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality. Originality/value The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.

An Overview of Small Nuclear Power Plants for Clean Energy Production: Comparative Analysis of Distributed Generation Technologies and Future Perspectives

There is a steady trend in the world to increase the share of distributed generation. The volume of self-generated energy commissioning is constantly growing, with projected increases in growth rates in the future. At the same time, demands for efficiency and environmental safety in new power plants are constantly increasing. In this regard, one of the promising areas for the development of distributed energy is the transition to small nuclear power plants (SNPPs). As in the case of wind and solar power plants, SNPP operations are not accompanied by emissions of toxic substances or greenhouse gases into the atmosphere. In addition, SNPPs consume a much smaller volume and mass of fuel compared to conventional small-capacity fossil fuel power plants. This paper describes the characteristics of the main types of distributed generation. The key technical and economic characteristics of existing and prospective small-capacity nuclear fuel facilities are systematized. The results of a comparative analysis of the cost of electricity produced by SNPPs and competing power plants are also presented. In addition, a number of promising regions of the Russian Federation for the introduction of SNPPS have been identified, and a review of the methods of energy storage for SNPPS, which are necessary when working in an isolated power system, has been carried out.

Evolutionary-state-driven multi-swarm cooperation particle swarm optimization for complex optimization problem

Particle swarm optimization (PSO) has been widely used to solve various practical engineering problems, but it has shortcomings such as premature convergence and poor global search ability. To solve these problems, evolutionary-state-driven multi-swarm cooperation particle swarm optimization (ESD-PSO) is proposed, using an adaptive multi-swarm cooperation mechanism (AMC) to improve the information exchange efficiency among all sub-swarms. In AMC, the population is randomly divided into multiple equal sub-swarms, and the best particle of a sub-swarm is used to evaluate its evolutionary state. When the number of stagnant sub-swarms reaches a threshold, all sub-swarms are adaptively regrouped. In addition, if a sub-swarm is stagnant for a long time without reaching the regroup condition, a stagnation compensation strategy (SCS) is triggered to enhance the stagnant sub-swarm diversity. In this case, a compensation sample is generated to help the stagnant sub-swarm to again search for promising regions. A competitive disturbance mechanism (CDM) is used to increase the solution accuracy. The performance of ESD-PSO is evaluated on the CEC2013 and CEC2017 test suites, and three engineering optimization problems. Experimental results show that ESD-PSO outperforms compared PSO variants.