External Elite Research Articles

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs

AbstractIn genomic selection (GS), the prediction accuracy is heavily influenced by the composition of the training set (TS). Currently, two primary strategies for building TS are used: one involves accumulating historical phenotypic records from multiple years, while the other is the “test‐and‐shelf” approach. Additionally, studies have suggested that optimizing TS composition using genetic algorithms can improve the accuracy of prediction models. Most breeders operate in open systems, introducing new genetic variability into their populations as needed. However, the impact of elite germplasm introduction in GS models remains unclear. Therefore, we conducted a case study in self‐pollinated crops using stochastic simulations to understand the effects of elite germplasm introduction, TS composition, and its optimization in long‐term breeding programs. Overall, introducing external elite germplasm reduces the prediction accuracy. In this context, test and shelf seem more stable regarding accuracy in dealing with introductions despite the origin and rate, being useful in programs where the introductions come from different sources over the years. Conversely, using historical data, if the introductions come from the same source over the cycles, this negative effect is reduced as long as the cycles and this approach become the best. Thus, it may support public breeding programs in establishing networks of collaborations where the exchange of germplasm will occur at a predefined rate and flow. In either case, the use of algorithms of optimization to trim the genetic variability does not bring a substantial advantage in the medium to long term.

Rural gentrification, the booming B&B industry and linked migration in China: To what extent can gentrification contribute to rural revitalisation?

AbstractAccompanying the functional transformation of rural areas from productivism to postproductivism, the rural areas with scenic views surrounding megacities are re‐evaluated in terms of their importance as destinations for visitors, especially in the case of booming bed and breakfast (B&B) industry in contemporary rural China. While private investment has generally led to improvements in the physical space and economic development of these villages, there is also concern about the negative effects of rural gentrification, such as the commercialisation of rural space, the alienation of rural culture and the displacement of indigenous residents. This study examines the multifaceted nature of rural gentrification by observing two villages within the Moganshan Mountain area—the birthplace of rural B&Bs in China, and explores how these processes have been leveraged to promote rural revitalisation. Research findings suggest that disparities in original resources and property rights allocation underpinning the B&B industries among the first‐wave gentrifiers and the local villagers, when viewed through the lens of linked migration, may lead to different socioeconomic outcomes. The B&B industry's growth can either be exclusive, primarily favouring gentrifiers, or complementary, fostering mutual benefits and learning opportunities for both gentrifiers and local villagers. Moreover, forming a collaborative network of rural villagers, returning migrants, and external elites can contribute to rural revitalisation through gentrification. These findings contribute to the theoretical understanding of rural gentrification by adding evidence from the Global South and provide insights to inform Chinese rural revitalisation policies, especially alternatives to traditional state‐led revitalisation projects.

Modeling and diagnosis of water quality parameters in wastewater treatment process based on improved particle swarm optimization and self-organizing neural network

The study designs a strategy to predict water quality parameters and identify faults in the activated sludge process. The proposed method combines the improved beetle antennae search (IBAS) with the dynamic particle swarm multi-objective optimization algorithm based on crowding distance (DMOPSO-CD), named DMOPSO-CD-IBAS. The fault diagnosis prediction interval of chemical oxygen demand (COD), biochemical oxygen demand (BOD5) and total suspended substance (TSS) was conducted by self-organizing fuzzy neural network with an efficient scheme for parsimonious (SOFNN-ESP). DMOPSO-CD enhances the efficiency of the beetle antenna search by adjusting inertia weights and acceleration factors, reaching the optimal state with a fitness value of 0.0961. Furthermore, the crowding distance sorting preserve the external elite set and update the global optimal values, upgrading the individual search mechanism of beetle antenna search to a group search mechanism to enhance population diversity. The proposed fault diagnosis method's effectiveness is validated through three specific faults (sensor fault of COD and BOD5, and the index of sludge expansion) during the sewage treatment process. The results of the prediction mean square error, root mean square error, and average absolute error of the proposed model were kept at about 0.011, 0.15 and 0.524, respectively, having better tracking performance and stronger robustness than SOFNN-ESP and backpropagation.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

Government participation and populist discourse transformation of radical left Syriza and radical right Anel

This study examines how the government participation within an EU country can affect the discourse of the left-wing and right-wing populist parties. We analyzed this question by tracing two Greek populist parties' discourse, the left-wing Syriza and the right-wing Independent Greeks (Anel), during the 2012-2019 period. We have split the examined period into two subperiods (2012 to July 2015, and August 2015 to 2019). The turning point was the Syriza-Anel government’s signature of the third EU bailout program in August 2015. The first hypothesis was that the government accession within the EU context would slightly decrease the populist logic of the two parties. The next two hypotheses referred that the EU leverage emerged by the signing of the bailout agreement will decrease the populist rhetoric of the two parties towards the external elites (mainly the EU). However, the right-wing populists (Anel)- even after this decrease- will target the external elites to a larger extent than the left-wing populists. To examine the hypotheses in-depth, we applied a combination of quantitative content analysis with qualitative discourse analysis. The findings have corroborated our three initial hypotheses. Simultaneously, the qualitative discourse analysis offered us some additional findings concerning the two parties' use of topos of “History” to increase their populist appeal within the electorate.

Quantum-Inspired Distributed Memetic Algorithm

This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon's rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component.

The energy, water supply, and ecology coordination for middle-long-term reservoirs scheduling with different connection modes using an elite mutation strategy-based NMOSFLA

Abstract Conventional reservoir operation emphasizes power generation (PG) with ignoring downstream river ecosystem and water supply benefits for sustainable development. Compared with the model defining water supply and ecological flow requirements as constraints, a novel long-term multi-objective scheduling model in complex parallel reservoir system (LTMOSCPRS) is developed to assess and achieve win–win and sustainable development for energy, water supply, and ecological benefits. The suitable and ideal ecological water requirements are calculated based on the requirement level index. Afterward, the novel multi-objective shuffled frog leaping algorithm (NMOSFLA) including renewed frog grouping, local search, and external elite frog set mutation strategies is proposed. Results indicate that three optimization objectives expose a mutual competing relationship. The benefit of the river ecosystem will increase at a loss of PG and water supply guarantee rate (WSGR). Therefore, the parallel reservoir system should be adjusted to improve the benefits of WSGR and ecological water spill and shortage (EWSS) with minimizing the loss of PG, simultaneously. Finally, the NMOSFLA is verified to outperform other compared methods at the solution diversity and convergence which is evaluated by multiple indicators. Overall, the NMOSFLA provides efficient reservoir operation schemes for decision-makers to select optimal trade-off schemes and feasible ways to solve the LTMOSCPRS.

Elite Archive-Assisted Adaptive Memetic Algorithm for a Realistic Hybrid Differentiation Flowshop Scheduling Problem

This research presents an original and efficient elite archive-assisted adaptive memetic algorithm (EAMA) to deal with a realistic hybrid differentiation flowshop scheduling problem (HDFSP) with the objective of total completion time minimization. In this scheduling problem, each job consists of multiple parts and the jobs are divided into different types. The manufacturing of a job is comprised of three consecutive stages: 1) parts fabrication on first-stage parallel machines; 2) parts assembly on second-stage single machine; and 3) job differentiation on one of third-stage dedicated machines. We provide a mixed-integer programming model, derive three lower bounds, and further present the EAMA metaheuristic for HDFSP. The EAMA is initialized heuristically, and its global exploration is performed by a differential evolution, which includes three newly designed operators: 1) elite-driven discretized differential mutation; 2) probability crossover; and 3) biased selection. To enhance the local search, an external elite archive is set and evolved in parallel with global exploration by a meta-Lamarckian learning-based adaptive multistage local search and a variable length-based adaptive block-insertion local search. After the global exploration and local exploitation, an elite sharing strategy is used to exchange the excellent information between population and elite archive, and an adaptive restart strategy is used to diversify the population. The influence of parameter setting on EAMA is surveyed by using an improved design-of-experiment. The statistical results from extensive computational experiments demonstrate the effectiveness of the special designs and show that EAMA performs more efficient than the existing algorithms in solving the problem under consideration.

Transactional colonialism in wind energy investments: Energy injustices against vulnerable people in the Isthmus of Tehuantepec

Energy production is a source of disputes across the world. Governments and firms argue that investing in wind energy contributes to the sustainable development of energy systems. However, wind farms perpetuate ongoing injustices and instigate new injustices. Vulnerable groups such as excluded and marginalised indigenous people can trace the injustices in low-carbon investments to a historical continuity of oppression and repression by internal and external elite groups. Based on a qualitative longitudinal study in the Isthmus of Tehuantepec in Oaxaca, Mexico, we expand our understanding of the energy justice framework in two ways. First, we show that cognitive justice is a vital dimension for understanding different ways of life, traditions and customs. Second, we propose the new concept of ‘transactional colonialism’, which emphasises the role of economic transactions between firms and economically motivated members of indigenous communities with the support of elite actors. This article provides new insights into the conflicting dynamics of wind energy investments in the Global South.

Direct method for uncertain multi-objective optimization based on interval non-dominated sorting

The interval optimization methods which describe the uncertainty by intervals are widely used in uncertain multi-objective optimization. Commonly, the interval values of the objective functions are substituted by approximate values, which will cause the loss of accuracy. To deal with the problem, a direct method is presented in this paper. In the framework of the multi-objective genetic algorithm, the upper and lower boundaries of the objective and constraint functions are found using the Monte Carlo stochastic simulation method and the constraints are handled by the degree of interval constraint violation; and then every two individuals from the combination population of the evolutionary population and the external elite population are compared using the interval comparison method. Afterwards, an interval non-dominated sorting based on the interval dominance relations is performed to sort the combination population into different non-dominated level. Furthermore, a general crowding distance is calculated for each individual to make individuals from the same non-dominated level comparable and also to maintain the diversity. The efficiency and accuracy of the proposed method is demonstrated by three test functions, the multi-objective structural optimization of the ten-bar truss, and the multi-objective structural optimization of a vehicle body for crashworthiness and lightweight.

A multi-objective adaptive evolutionary algorithm to extract communities in networks

Community structure is one of the most important attributes of complex networks, which reveals the hidden rules and behavior characteristics of complex networks. Existing works need to pre-set weight parameters to control the different emphasis on the objective function, and cannot automatically identify the number of communities. In the process of optimization, there will be some challenges, such as premature and inefficiency. This paper presents a multi-objective adaptive fast evolutionary algorithm (F-SGCD) for community detection in complex networks. Firstly, it transforms the problem of community detection into a multi-objective optimization problem and constructs two objective functions of community score and community fitness. Secondly, an external elite gene pool is introduced to store non-inferior solutions with high fitness. At the same time, an adaptive genetic operator is executed to return a set of non-dominant solutions compromised between the two objective functions. Finally, a Pareto optimal solution with the highest modularity is selected and decoded to generate a set of independent subnetworks. Experiments show that the multi-objective adaptive fast evolutionary algorithm greatly improves the accuracy of community detection in complex networks, and can discover the hierarchical structure of complex networks better.

Dynamic environmental economic dispatch of hybrid renewable energy systems based on tradable green certificates

China is accelerating the formulation of a renewable portfolio standard to promote the development and utilization of renewable energy. The implementation of the standard and corresponding tradable green certificates will change the scheduling procedures of thermal power and other renewable energy generators in the electric power system. This paper formulates a multi-objective dynamic economic emission dispatch model for wind-solar-hydro power under tradable green certificates. Weibull, Beta and Gumbel distributions are used to simulate the random behavior of wind, solar and hydro power, respectively, thus taking into account the overestimation and underestimation costs caused by the randomness of renewable energy. Moreover, a new multi-objective moth-flame optimization (MOMFO) based solution is proposed according to the characteristics of the dispatch model. The MOMFO is based on the external elite reservation and Pareto dominance. To improve the search capability, the strategy of average entropy initialization, Lévy flight and variable scale chaotic strategy are introduced. A heuristic dynamic relaxation method is proposed, which is used to handle the complex constraints of the model. Finally, the feasibility of the proposed scheduling model and the effectiveness of the algorithm are illustrated using examples of different scheduling situations based on IEEE 39-bus standard system.

Investment Migration and Subnational Jurisdictions

This chapter sets out to explain the position of small islands, focusing on their economic and citizenship dependence on metropolitan states. These small jurisdictions argue that their market operations could be legitimately described as improving the institutional ‘efficiency’ of financial markets. Moreover, and quite realistically, they have limited development choices to consider and are effectively playing to their narrow competitive edge, as rendered by their sub-national status, small size and islandness. These acts, suitably ring fenced, are expressions of ‘creative governance’, a blatant and market driven exploitation of political geography for economic, fiscal or strategic gain. Furthermore, the ‘governmentality’ of the territorial state – its clout and reach over the behaviour of its citizens – is being sorely tested, with the proliferation of offshore banking and dual nationality/multi-passporting for various clients, ranging from transnational corporations to ‘cosmopolites’ to those unable or unwilling to tolerate particular leaders at home. Such practices are being also abetted by the politico-economic elite at the helm of small, often island, jurisdictions: these policy shapers sense – and are reminded by the international corporate world – that they have a rare competitive advantage in widening their jurisdiction’s tax base by reducing tax rates and offering citizenship schemes. Criticism and conceptual provocations, of course, abound, and especially since the 2008 global financial crisis, when leading economies have been naming and shaming individual and corporate tax offenders in attempt to collect their tax. The very principle of ‘offshore financial services’, for all its veneer of respectability, has been subjected to disrepute because the legal products being offered are tantamount to acts of ‘selling sovereignty’. The practice has been sometimes viewed as part of a suite of desperate measures taken by small and chronically vulnerable and cash strapped territories, often connived by external elites who can capture the state to do their bidding. That said, citizenship by investment programs have recently been proliferating and this trend is expected to continue. The extra-territoriality potential offered by such spaces as the high seas, the Moon and the Planet Mars renders them attractive locales for novel governance regimes that may wish to escape the clutches of rapacious, tax-seeking states. Meanwhile, here on Planet Earth and on solid ground, subnational jurisdictions are ‘territories sui generis’ that already offer their own opportunities for creative endeavour.

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Research of Youth Community Education and Development of Local Cultural Industries

Since the Executive Yuan of Taiwan began promoting “Six-star Plan for New Communities in Taiwan” in 2005, learning systems have been established for school, community, and local education. In recent years, this has affected youths in understanding public affairs and in actively participating in community service. Thus, the researcher combined learning experiences and youth action based on love of the locale, holding a 2-day woodworking workshop at the “camphor office” in Jiji Township in Nantou County. With the historical site as the base of action, the study explores the developmental trajectory of the practice of youth action and local culture and creativity.This study used action research and participatory design, through the cultural symposium of community education, youths can practically participate in design and reinvigorate old buildings in the communities. The study found the following issues: (1) Arranging for courses relating to community education can consolidate community consensus; (2) People have insufficient industrial knowledge, and insufficient knowledge about the historical contexts of past local industries; (3) Community education may be customized in design based on locality. The following suggestions are provided: (1) Youth action and external elite design can reinvigorate contextual space; (2) Through community education courses, strengthen industrial and cultural knowledge; (3) The necessary paths to promoting community sustainability are to elevate overall community quality and establish specialty developments. In summary of these issues and suggestions, the study provides maximum benefits to expansion of community education and local cultural industries.

MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

The Legitimation of Ethnicity: The Case of the Cornish

AbstractIn this article we examine the complex processes involved in small‐scale ethnicity emergence and legitimation, and highlight the multi‐dimensional elements present in moving from a strong regional identity to an externally legitimate ethnic group. We use Cornwall as a case study: administered as an English county, there has been a historic ethno‐cultural movement for recognition alongside recent inclusion in national statistics; however, legitimation by external elites has been problematic. The first sections outline the Cornish as a group; we argue that however one conceptualises ‘ethnicity’, the people of Cornwall must constitute such a group. We examine the dichotomous effects of the interplay between strong regional assertion and a Cornish ethnicity more formally. In the latter sections we apply these arguments to broader sociological discussions around the legitimation of particular groups, and show that the Cornish are indicative of the wider theoretical literature. In conclusion, we assert that the Cornish are representative of the push/pull mechanisms felt acutely in any core/periphery power relations, and should be seen as central to emerging small‐scale ethnic groups more generally.

An efficient multi-objective optimization approach based on the micro genetic algorithm and its application

In this paper, an efficient multi-objective optimization approach based on the micro genetic algorithm is suggested to solving the multi-objective optimization problems. An external elite archive is used to store Pareto-optimal solutions found in the evolutionary process. A non-dominated sorting is employed to classify the combinational population of the evolutionary population and the external elite population into several different non-dominated levels. Once the evolutionary population converges, an exploratory operator will be performed to explore more non-dominated solutions, and a restart strategy will be subsequently adopted. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to NSGAII. Eventually, this approach is applied to the structural optimization of a composite laminated plate for maximum stiffness in thickness direction and minimum mass.

A multiobjective optimization solver using rank-niche evolution strategy

A rank-niche evolution strategy (RNES) algorithm has been developed in this paper to solve unconstrained multiobjective optimization problems. A required number of Pareto-optimal solutions can be generated by the algorithm in a single run. In addition to the operations of recombination, mutation and selection used in original evolution strategy (ES), an external elite set which contains a given number of non-dominated elites is updated and trimmed by a clustering technique to maintain a uniformly distributed Pareto front. The fitness function for each individual contains the information of rank and crowding status. The selection operation using this fitness function considers the superiority and distribution simultaneously. Eight test problems illustrated in other papers are used to test RNES. For some test problems the Pareto-optimal solutions obtained by RNES are better than those obtained by GA-based algorithms.

German Unification and Bureaucratic Transformation

This paper elaborates the role of bureaucracy in the process of German unification since 1990, first by analyzing the interplay between politics and ministerial bureaucracy in negotiating the unification treaty including its civil service related part with the German Democratic Republic; second, by describing the implementation of administration policy (comprising personnel and organization policies), the elite import and transfer of administrative structures from the west are emphasized as essential elements in transforming the East German polity. Structural conservatism rather than innovation can be observed as a result. With respect to the broader topic of regime change, elites, and bureaucracy, it is argued that, owing to the dual nature of transition in East Germany (implosion plus unification), the availability of a counter-bureaucracy and an external elite reservoir (as well as western capital) accounts for the absence of political resistance of the old apparatuses that presently bother East European countries. However, programmatic and financial policies have to accompany administration policy to become fully effective.