Peer Competitors Research Articles

Split-Level Evolutionary Neural Architecture Search With Elite Weight Inheritance.

Neural architecture search (NAS) has recently gained extensive interest in the deep learning community because of its great potential in automating the construction process of deep models. Among a variety of NAS approaches, evolutionary computation (EC) plays a pivotal role with its merit of gradient-free search ability. However, a massive number of the current EC-based NAS approaches evolve neural architectures in an absolutely discrete manner, which makes it tough to flexibly handle the number of filters for each layer, since they often reduce it to a limit set rather than searching for all possible values. Moreover, EC-based NAS methods are often criticized for their inefficiency in performance evaluation, which usually requires laborious full training for hundreds of candidate architectures generated. To address the inflexible search issue on the number of filters, this work proposes a split-level particle swarm optimization (PSO) approach. Each dimension of the particle is subdivided into an integer part and a fractional part, encoding the configurations of the corresponding layer, and the number of filters within a large range, respectively. In addition, the evaluation time is greatly saved by a novel elite weight inheritance method based on an online updating weight pool, and a customized fitness function considering multiple objectives is developed to well control the complexity of the searched candidate architectures. The proposed method, termed split-level evolutionary NAS (SLE-NAS), is computationally efficient, and outperforms many state-of-the-art peer competitors at much lower complexity across three popular image classification benchmark datasets.

Two-stage many-objective evolutionary algorithm: enhanced dominance relations and control mechanisms for separated balance

Although the multiobjective evolutionary algorithms (MOEAs) have been proved to bring promising prospects for solving multiobjective optimization problems (MOPs), the performance of the algorithm deteriorates sharply in high-dimensional objective space due to the weak selection pressure and the unregulated balance, which is caused by the increase of objective space dimension. Some current MOEAs with two-stage strategy (TS) strive to address above issues by dividing the evolutionary process into two independent stages, in which convergence and diversity are handled separately within successive generations of different stages. However, TS-MOEAs have some weaknesses, such as sensitivity to stage division, and incomplete separation of convergence and diversity. In this paper, TS/KW-MaOEA is proposed for solving many-objective optimization problems (MaOPs), which keeps TS as the central and equips a perfect control mechanism for separated balance. More specifically, TS/KW-MaOEA can automatically adjust the balance trend and provide appropriate selection pressure for MaOPs according to the Kondratiev wave (KW) search model and the objective space dimension. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out against seven state-of-the-art many-objective optimization algorithms on 15 benchmark problems with up to 30 objectives. Experimental results indicate that the proposed algorithm is highly competitive against peer competitors.

EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation

Medical images are crucial in clinical practice, providing essential information for patient assessment and treatment planning. However, manual extraction of information from images is both time-consuming and prone to errors. The emergence of U-Net addresses this challenge by automating the segmentation of anatomical structures and pathological lesions in medical images, thereby significantly enhancing the accuracy of image interpretation and diagnosis. However, the performance of U-Net largely depends on its encoder–decoder structure, which requires researchers with knowledge of neural network architecture design and an in-depth understanding of medical images. In this paper, we propose an automatic U-Net Neural Architecture Search (NAS) algorithm using the differential evolutionary (DE) algorithm, named EU-Net, to segment critical information in medical images to assist physicians in diagnosis. Specifically, by presenting the variable-length strategy, the proposed EU-Net algorithm can sufficiently and automatically search for the neural network architecture without expertise. Moreover, the utilization of crossover, mutation, and selection strategies of DE takes account of the trade-off between exploration and exploitation in the search space. Finally, in the encoding and decoding phases of the proposed algorithm, different block-based and layer-based structures are introduced for architectural optimization. The proposed EU-Net algorithm is validated on two widely used medical datasets, i.e., CHAOS and BUSI, for image segmentation tasks. Extensive experimental results show that the proposed EU-Net algorithm outperforms the chosen peer competitors in both two datasets. In particular, compared to the original U-Net, our proposed method improves the metric mIou by at least 6%.

A dual surrogate assisted evolutionary algorithm based on parallel search for expensive multi/many-objective optimization

Numerous optimization problems in the real world involve multi-objective and computationally expensive simulations (i.e., expensive multi-objective optimization problems). This paper purposes a dual surrogates-assisted evolutionary algorithm (SAEA) based on parallel search, termed DSAEA-PS, for this issue. Approximation and classification are two main implementation forms of surrogate models, but the existing methods of expensive multi-objective optimization only apply one kind of them, and scarce works have paid attention to combining approximation and classification to improve the optimization performance. In the proposed algorithm, to enhance the prediction accuracy and reliability, both the approximation model and classification model are applied to cooperate to provide the quality and uncertainty information of candidate solutions. Meanwhile, the parallel search based on heterogeneous multi-objective evolutionary algorithms is introduced for better exploration of the decision space. In addition, combined with the strengthened dominance relation (SDR), a sampling strategy that comprehensively considers the quality of candidate solutions and their uncertainty information is proposed. Experimental results with five peer competitors on a set of widely-used benchmark problems demonstrate the ability of DSAEA-PS. Furthermore, DSAEA-PS is adopted for a five-objective blended-wing-body underwater glider design problem that involves time-consuming simulations of fluid dynamics and structural strength. A series of high-performance solutions obtained from DSAEA-PS verifies its effectiveness on engineering applications.

An Analysis of Starbucks Marketing Strategy and Innovation in the Chinese Market

As the number of Starbucks shops in China continues to increase, Starbucks focus on creating high-end products has neglected some of the basic needs of the consuming public. With negative press, the emergence of more and more peer competitors, and the impact of the new crown pneumonia epidemic, Starbucks should change its current status quo, improve its reputation, make corporate innovations, and change marketing strategies. This paper will analyze Starbucks current internal and external environments as well as its marketing strategy using a combination of theory and practice, using SWOT analysis theory and a comparison with competitors such as Luckin Coffee and %Arabica Coffee to elaborate, and give some feasible suggestions for corporate innovation. Through SWOT analysis, Starbucks should adopt a coffee + strategy to bundle sales, increase the variety of coffee derivatives, expand partner brands, and take advantage of the Internet to create a Netflix brand to drive traffic.

NATO’s Approach to Multi-Domain Operations: From the Perspective of the Economics of Alliances

ABSTRACT This study presents a model that incorporates the characteristics of multi-domain operations (MDO) and analyzes the impact of the North Atlantic Treaty Organization’s (NATO) approach to MDO on the behavior of its member countries from the perspective of the economics of alliances. NATO is wary of authoritarian states and prepared for the great power competition with them, as shown in its current strategic concept. To prepare for a possible future war against peer competitors, NATO is strengthening its capabilities to conduct MDO, in which cross-domain synergy is a key factor. The model presented in this study relates the cross-domain synergy in MDO and the complementarity between the defense activities in multiple domains using organizational and public economics methods. The implication of this model is that if the defense activities in different domains are complementary, one member country’s increasing defense activities in one domain can increase another’s defense activity in another domain that is complementary to that domain. It is particularly crucial for NATO’s future military buildup: NATO’s approach to MDO will make defense burden-sharing fairer because one member’s military buildup will motivate other members to build up theirs.

Humanitarian Challenges of Great Power Conflict: Signs from Ukraine

Abstract Until the Russian invasion of Ukraine, there was little evidence of what a conventional war between the regular forces of peer competitors might look like today. After the total war of the twentieth century, the evolution of precision-guided munitions and drones set up the possibility of a new ideal type of conflict, in which U.S.-NATO coalitions could quickly defeat the regular forces of much weaker opponents, largely from a distance, while avoiding excess loss of civilian life. “Smart” weapons created the perception that when civilians were killed, this was an operational failure. Russia's approach to war, however, has not put a high priority on avoiding civilian casualties, but has shown that precise weapons could be used deliberately to target civilian infrastructure in ruthless and coercive air campaigns. In this essay, I suggest that the Russia-Ukraine War provides insight into what a major power war would look like. I contrast the two distinct approaches represented by Ukraine, strengthened by NATO weapons and informed by its concepts, and Russia, with its readiness to attack civil society. I focus on the resulting humanitarian disaster in Ukraine, where more than one-quarter of the population has been displaced and where Ukrainians in Russian-occupied territories have reported thousands of instances of war crimes. I conclude by considering the likelihood and potential consequences of Russia's use of nuclear weapons.

Great Power Competition in the Gulf of Guinea

The erosion of unipolarity in conjunction with the rise of new as well as great powers who are “near peer competitors” in an increasingly multipolar world has contributed to the changing conceptualization of war. This development coupled with an increasingly uncertain international milieu, has brought significant changes in strategic thinking as new modes of warfare, new technologies and new adversaries in different places have come to the fore. These places of strategic consequence include the Gulf of Guinea which has the potential to become an important theatre for great power rivalry as a major hydrocarbon hub, an important trading route and also a source of economic diversification. The Gulf of Guinea has likewise seen increased securitisation as great powers increase their defence engagements across the globe. Moreover, the intensification of great power competition is likely to make each theatre more consequential as a competitive edge may be gained from greater engagement. The research has endeavoured to analyse the geopolitical background of the great power competition in the Gulf of Guinea; to assess the current nuances and dimensions of great power competition in the Gulf of Guinea; to examine the potential challenges and prospects that accompany great power competition and their implications for countries in the region; and to consider the long-drawnout ramifications and implications of great power competition in the Gulf of Guinea.

What drives horizontal logistics collaboration? A grounded theory analysis of Chinese logistics service providers.

Horizontal logistics collaboration is characterized by the highly professional cooperation of logistics service providers at the same level of the supply chain, which is an effective way to optimize the development and sustainability of modern logistics. Conducting a coding analysis of data from 41 in-depth interviews, this study constructs a theoretical framework of the driving force of horizontal logistics collaboration among Chinese logistics service providers to synchronously improve service, market, efficiency, and emergency competitiveness through cooperation with peer competitors to enhance sustainable comprehensive competitiveness. The four driving forces encourage logistics service providers to adjust their thoughts in the planning stage, change their behavior in the implementation stage, and transform their strategy in the evaluation stage. The results also indicate differences between China and other countries in the driving force, effective utilization of logistics workers, cross-institutional collaboration of state-owned enterprises, and integration of artificial intelligence technology. Finally, we propose relevant recommendations for managers.

An Empirical Study of Software Companies’ Merger and Acquisitions in the Software-as-a-Service Market

This study examines the herding behavior of software companies when they merge and acquire (M&A) SaaS firms to compete in the market. Through an empirical analysis of 71 companies in SIC 737 industries from 2003- 2017, we find that firms try to imitate their peer competitors’ M&A activities and their imitation decisions are contingent on the resources and capabilities. Specifically, firms with more slack resources are more likely to develop SaaS products and services by themselves instead of acquiring an existing SaaS firm. Creating their own SaaS products could bring more stable and reliable profits in the long term, even though firms may have low profit margins in the short term. Thus, these firms do not consider acquiring the developing process through M&A. Our findings have managerial implications for on-premises companies in their transformation to cloud-based businesses.

An Angle-based Many-Objective evolutionary algorithm with Shift-based density estimation and sum of objectives

Due to the curse of dimensionality, the existing evolutionary algorithms have difficulties in balancing convergence and diversity in many-objective problems. To address this shortcoming, this paper proposes an efficient many-objective optimizer named MaOEA-ASS. In the MaOEA-ASS, the angle-based selection strategy is used to obtain solutions with good diversity from the population. In addition, the combination of the shift-based density estimation and the sum of objectives, which uses the iteration information and emphasis the distribution of solutions, is employed to obtain the high-quality solutions approximating the optimal Pareto solutions. The proposed MaOEA-ASS is compared with eight state-of-the-art many-objective optimization algorithms (MaOEAs) on the DTLZ and WFG test suites, and its performance is verified on a practical many-objective problem. The experimental results demonstrate that the proposed MaOEA-ASS has a superior performance over the peer competitors on all considered many-objective problems.

A Methodological Framework for Parametric Combat Analysis

This work proposes, explains, and demonstrates a methodological framework which affords decision-makers and policy-makers the opportunity to accelerate the tempo and enhance the quality of the U.S. Department of Defense acquisitions community. The effective implementation of this framework will augment the warfighting capability of the U.S. Department of Defense against peer competitors and advance the security posture of the United States. This work provides a theoretical foundation for explaining the distribution of potential outcomes for certain types of warfare while contextualizing the research to the space domain. This framework utilizes reliability models to account for natural attrition while using combat models to account for induced attrition. The use of game-theoretic concepts enables the evaluation of heterogeneous force structures.

A Methodological Framework for Parametric Combat Analysis

This work proposes, explains, and demonstrates a methodological framework which affords decision-makers and policy-makers the opportunity to accelerate the tempo and enhance the quality of the U.S. Department of Defense acquisitions community. The effective implementation of this framework will augment the warfighting capability of the U.S. Department of Defense against peer competitors and advance the security posture of the United States. This work provides a theoretical foundation for explaining the distribution of potential outcomes for certain types of warfare while contextualizing the research to the space domain. This framework utilizes reliability models to account for natural attrition while using combat models to account for induced attrition. The use of game-theoretic concepts enables the evaluation of heterogeneous force structures.

An integrated framework with evolutionary algorithm for multi-scenario multi-objective optimization problems

Multi-objective optimization problems often load in the multi-scenario environment, and they can be modeled as multi-scenario multi-objective optimization problems (MSMOs). So far, existing research on MSMOs mainly focuses on the specific method in engineering applications, and general methods are quite scarce. This paper develops an integrated information-based evolutionary framework to find a set of scenario-based non-dominated solutions for MSMOs. Sequentially, a scenario-based dominance principle evolutionary algorithm and a decomposition-based evolutionary algorithm are combined with the framework to construct two integration algorithms, respectively. Furthermore, the crucial components in both algorithms are developed under the multi-scenario environment, such as scenario-based constraint handling, scenario-based dominance concept, scenario-based crowded degree and multi-scenario decomposition method. Additionally, we design a novel comprehensive evaluation indicator for MSMOs. Finally, the proposed framework is tested on numerous artificial and engineering problems. Experiments demonstrate that the framework has superiority over four existing state-of-the-art peer competitors concerning the indicator. Two integration algorithms are able to find a set of good scenario-based non-dominated solutions.

Planning for protraction

As Sino-US relations have deteriorated, concerns have grown in Washington over its ability to defeat China in a major conflict. A conflict between such peer competitors would likely become a protracted war of attrition drawing on all dimensions of national power, but this reality has yet to receive a sufficient degree of analytical attention. In this Adelphi book, Iskander Rehman provides a historically informed and empirically grounded study of protracted great-power war, its core drivers and characteristics, and an examination of the elements that have most often determined a competitor’s long-term strategic performance. A detailed analysis of the contemporary Sino-US rivalry assesses how both parties might fare in the event of a protracted war, while highlighting some of its key significant differentiating aspects – most notably its nuclear and cyber dimensions.

China, the US and protracted war: a comparative evaluation

As Sino-US relations have deteriorated, concerns have grown in Washington over its ability to defeat China in a major conflict. A conflict between such peer competitors would likely become a protracted war of attrition drawing on all dimensions of national power, but this reality has yet to receive a sufficient degree of analytical attention. In this Adelphi book, Iskander Rehman provides a historically informed and empirically grounded study of protracted great-power war, its core drivers and characteristics, and an examination of the elements that have most often determined a competitor’s long-term strategic performance. A detailed analysis of the contemporary Sino-US rivalry assesses how both parties might fare in the event of a protracted war, while highlighting some of its key significant differentiating aspects – most notably its nuclear and cyber dimensions.

Great-power success in protracted warfare: key drivers and core components

As Sino-US relations have deteriorated, concerns have grown in Washington over its ability to defeat China in a major conflict. A conflict between such peer competitors would likely become a protracted war of attrition drawing on all dimensions of national power, but this reality has yet to receive a sufficient degree of analytical attention. In this Adelphi book, Iskander Rehman provides a historically informed and empirically grounded study of protracted great-power war, its core drivers and characteristics, and an examination of the elements that have most often determined a competitor’s long-term strategic performance. A detailed analysis of the contemporary Sino-US rivalry assesses how both parties might fare in the event of a protracted war, while highlighting some of its key significant differentiating aspects – most notably its nuclear and cyber dimensions.

A multistage evolutionary algorithm for many-objective optimization

Convergence and diversity are of high significance to many-objective optimization, which are considered by most state-of-the-art many-objective evolutionary algorithms (MaOEAs) simultaneously. However, it is not easy to balance them during the optimization process due to their conflicting nature. This study proposes a multistage MaOEA to address this issue, where convergence and diversity are processed respectively at different optimization stages. At the first stage, the population approaches Pareto front rapidly and the diversity is ignored. After the population is converged, the diversity will be emphasized by applying the decision variable clustering method at the second stage. When the population achieves high convergence and diversity, the algorithm will enter the last stage, where the quality of the solution set is fine-tuned by substituting those solutions with worse convergence and diversity degrees. As demonstrated by the experimental results with peer competitors on common benchmark problems, that the proposed algorithm is promising.

A self-adaptive multi-objective dynamic differential evolution algorithm and its application in chemical engineering

This paper proposes a new multi-objective dynamic differential evolution algorithm with parameter self-adaptive strategies, named SA-MODDE. All components of the algorithm are synergically designed to reach its full potential, containing parental selection, mutation strategy, parameter setting, survival selection, constraint handling, and termination criteria. The improvement measures emphasize exploiting Pareto dominance information more efficiently. Particularly, parameter adaptation schemes are introduced based on both prior knowledges of current individual and feedback information on previous promising solutions, and their effectiveness is validated by comparison with three fixed-parameter combinations. Extensive numerical tests are conducted on multiple test suites with five state-of-the-art peer competitors. The statistical results demonstrated that the SA-MODDE exhibits good proximity and diversity in dealing with benchmark functions with various characteristics. Three industrial (bio)chemical processes, including two optimal control and one reformulated constrained tri-objective, are investigated to show the feasibility and robustness of the SA-MODDE.

THE STRATEGIC EFFICACY OF DRONES FOR US GRAND STRATEGY

This paper assesses the role of drones in furthering or undermining US grand strategy. This is against the backdrop of the thinking that contemporary use of drones in the context of post 9/11 era undermine the successive US administration’s strategic objectives as evidenced by the rise of anti-Americanism in Muslim world, proliferation of drones by US near peer competitors, civilian death toll and weakening support for the US in targeted countries. This implies that while drones has played a historical and significant role for the US in power projection and asserting its unilateralism and military hegemony when dealing with rogue states and terrorist groups post 9/11, the political and strategic utility of drone strikes for US grand strategy is not apparent. Thus, this paper posits that though armed drones has played a quintessential role as a key instrument of statecraft for facilitating US offensive strategy in targeted states, the aftermath of drone strikes and its controversial aspects engender inimical outcomes that serve to undermine US strategic objectives. Based on qualitative analysis of secondary data, the paper questions the wisdom and benefits of using and shifting greater reliance towards armed drones, as a pathway for furthering US grand strategy.