Competitive Problem Research Articles

Markets of agro-industrial complex products in conditions of geoeconomic fragmentation of the international trade

For decades, international trade has contributed to the reduction of global poverty and the income gap between countries, and the development of intercountry exchange of agricultural products has largely ensured the solution of problems of poverty and food security. At the same time, at the present stage, a high degree of dependence of many developing countries on food imports has occured, while its global exporters have faced a number of restrictions on the supply of their agricultural products. This article analyses the impact of geoeconomic fragmentation of the international trade on the development of exports and imports of products of the agro-industrial complex (hereinafter referred to as AIC). In particular, the problems of competition in the world agro-food markets are discussed in a new context, since the growing problems of monopolisation in certain segments of the AIC disrupt the stability of agricultural markets. Exclusive attention is paid to the key aspects of the application of non-tariff measures in regulating trade of the agricultural products, which, in addition to the protective role, can have a distorting effect on world prices. Our analysis places particular emphasis on the hidden costs of global agri-food systems, which necessitate their rapid modernisation to ensure sustainability in the development of the agricultural markets and to address global food security issues.

Alternativizing markets: the framing of moral commerce

Abstract How are moral markets organized? While previous research has shown that moral markets combine conventional and alternative elements, it has failed to explain how these hybridizations can ensure the organization of moral markets. To this end, we take an economic-sociological approach using ethnographic materials from a study of Swedish Reko-rings, a direct-to-customer market for local foods, to analyse how actors address the key coordination problems of value, competition and cooperation. The analysis shows how participants of these markets enact an ‘alternative framing’, emphasizing economic certainty as instrumental for moral certainty, which enables them to develop and implement ‘alternativized’ solutions to market coordination problems. Thereby, participants make economic and moral values certain by making the former instrumental for the latter. We advance previous research by showing that moral markets require not just hybridization, but hybridization guided by a specific economic-moral framing that enables these markets to avoid co-option.

Penentuan Strategi Pegembangan UKM Tempe Pak Fadli Menggunakan Metode Analisis SWOT dan Boston Consulting Group (BCG)

Micro, small, and medium enterprises (MSMEs) are an important part of the Indonesian economy to improve people's welfare. Pak Fadli's Tempe UKM located in Parerejo Village, Purwodadi District, faces the problem of fierce competition in the tempeh industry and the lack of information obtained by business actors regarding modern technology in the marketing system. Using SWOT Analysis and BCG Matrix, this study aims to find out internal and external factors and what strategies can be used for the growth of Mr. Fadli's tempe business. The results showed using a SWOT Analysis that Pak Fadli's Tempe SMEs had a higher total strength score (4.08) than a total weakness score (3.17), which placed him in Quadrant I. This shows that Pak Fadli's Tempe SMEs should use all their strengths to take advantage of the best opportunities. According to Matrix BCG's calculations, Pak Fadli's Tempe SMEs are in Quadrant III (Cash Cows) with a total market share value of 1.03 and a market growth value of 2.4%. Because it has a large market share but is in a slow-growing sector. Increasing market share and expanding distribution networks is one strategy that can be used. With this research, it is hoped that it can provide recommendations for development strategies and the right marketing strategy for Pak Fadli Tempe SMEs to be able to compete with their competitors.

The Impact of Green Entrepreneurial Orientation and Greenmarket Orientation on SME's Performance: The Role of Green Innovation

Protecting the environment has turned into a competitive market problem, affecting the success of small businesses. Much research has been done on the direct links between green entrepreneurial orientation, green market orientation, green innovation, and SME success. However, few studies look at the indirect links through the lens of green innovation. This study aims to find out if green market orientation and green entrepreneurial orientation impact the success of small and medium-sized businesses. PLS-SEM is used to test the theories quantitatively. Conversations with Batik SME business owners in Ponorogo City were used to gather data. Thirty people took part in this study. The results show that green market orientation and green entrepreneurial orientation have both direct and indirect effects on the success of small businesses. Green innovation also has a direct impact on the success of small and medium-sized enterprises, as well as affects green business orientation and green market orientation. Keywords: green entrepreneurial orientation, green market orientation, green innovation, SME performance

ОСОБЛИВОСТІ АУДИТУ ДОХОДІВ, ВИТРАТ І ФІНАНСОВИХ РЕЗУЛЬТАТІВ ВІТЧИЗНЯНОГО СІЛЬСЬКОГОСПОДАРСЬКОГО ПІДПРИЄМСТВА В УМОВАХ ВОЄННОГО СТАНУ

The article is devoted to the study of the audit of income, expenses, and financial results of agricultural enterprises in Ukraine during the martial law. The main problems of the domestic agricultural industry, caused by the specifics of the industry, are identified: deterioration of economic conditions, economic difficulties, competition problems, infrastructural limitations, personnel problems, difficulty in obtaining permits and certification, limited access to modern technologies. It is substantiated that the problems of the organization of accounting and management of agricultural enterprises are represented by insufficient automation of accounting and control processes, insufficient investment attractiveness of these enterprises in reporting, the complexity of accounting for supply processes, production and formation of costs, the complexity of obtaining loans, the complexity of tax accounting. The identified problems of the functioning of domestic enterprises in the conditions of martial law: the threat of security for workers and resources, the complication and increase in the price of the supply and implementation process, the loss of qualified labor, economic difficulties, the instability of legislation, the cancellation of benefits. It was established that the state of war in Ukraine causes additional problems for auditing: changes in the conditions of audit activity and audit risks, the complication of forecasting and assessment of financial results, the need to assess the impact on financial reporting of changes in accounting policies and management strategies, the need for additional audit procedures to determine and settlement of new risks. The work analyzes the peculiarities of the audit process of financial reporting of an agricultural enterprise, its income, expenses, and results of activity. The study showed that the main factor influencing the quality of the audit is the use of effective audit procedures. The work presents directions for optimizing the audit due to its digitalization, identifies problems that may arise, and suggests ways to solve them.

Population-based algorithm for discrete facility location with ranking of candidate locations

Facility location problems are mathematical optimization problems that involve finding the best locations for facilities (e.g., factories, warehouses, stores) to serve customers within a given geographic area. The goal is typically to minimize costs, maximize efficiency, or optimize other objectives. Facility location problems can vary in several ways, including customer behavior rules, the type of search space, and constraints on locations for new facilities being located. These variations directly impact the complexity of the problem and the appropriate solution methods that can be used to tackle the problem. This research is focused on the discrete competitive facility location problem for an entering firm, which is a crucial scenario for new firms entering the existing market. The goal is to strategically locate new facilities to maximize their profit, while considering existing competitors. A new random search heuristic algorithm to approximate the optimal solution for discrete competitive facility location problems for firm expansion has been developed. The algorithm extends its precursor that ranks potential locations for the new facilities depending on their usefulness and uselessness in creating new solutions in the past. The new algorithm uses a population to handle and reuse the best solutions found so far and new strategies for ranking potential locations, considering features of the solutions in the population. The designed algorithm has been investigated by solving competitive facility location problems actual for an entering firms using real geographical data.

Competition in the vegetable market and challenges for the industry: a bibliometric analysis of dominant publications

Purpose. The purpose of this paper is to identify current and future directions of research using bibliometric analysis of publications indexed in Scopus and Web of Science databases on the problems of competition in the vegetable market and industry development, as well as dominant topics. Methodology / approach. A comprehensive review of 1246 publications indexed in the Scopus and Web of Science databases was carried out using the VOSviewer and Bibliometrix software packages. The analysis was carried out using Microsoft Excel, Canva, and Datawrapper programs, and after careful consideration, 67 works were selected for in-depth study. Results. The bibliometric analysis of the published publications revealed research trends; using software analysis, by periods, the main directions of research carried out in the last 60 years were evaluated; participation of countries in the process of scientific research was determined; research clusters were identified according to the countries and authors of dominant publications. The paper provides conclusions about current research problems in the publications on competition in the vegetable market and industry challenges, and offers recommendations for future research directions. It was determined that conducting a similar bibliometric research in developing countries such as Georgia is impossible at this stage due to the lack of a local scientific database, also they do not have enough abilities to publish a large number of articles indexed in Scopus and Web of Science. Impeding factors were identified and ways to solve the problem were determined. Originality / scientific novelty. Based on the bibliometric analysis, for the first time, investigation is conducted to examine the competition in the vegetable market and industry challenges. The bibliometric analysis revealed that the research field has been developing steadily and continuously over the years, with narrow and specialised topics being transformed in a multifaceted way in the wake of global issues. The research area is geographically unevenly concentrated in the countries of North America, Europe and Asia. Practical value / implications. The obtained results will help stakeholders, in particular: (і) researchers to identify current issues in the industry; (іі) the major players in the vegetable market in assessing the level of market competition; and (ііі) political actors in determining the characteristics of the industry for the implementation of innovative projects.

A tree seed algorithm with multi-strategy for parameter estimation of solar photovoltaic models

Tree seed algorithm, which is one of the metaheuristics algorithms recently proposed for the solution of continuous optimization problems, has an effective algorithmic structure inspired by the relation between trees and seeds. At the same time, the use of two different solution generation mechanisms by depending on the control parameter in TSA aims to balance the exploration and exploitation capabilities of the algorithm. However, when the structure of the algorithm is examined in detail, it is seen that there are some disadvantages such as loss of population diversity and getting stuck in local minimums. To overcome these disadvantages in the basic algorithm, three different approaches (self-adaptive weighting mechanism, chaotic elite learning approach and experience-based learning method) were proposed to TSA under the name of multi-strategies in this study. The algorithm improved with these approaches is named as the multi-strategy-based tree seed algorithm (MS-TSA). MS-TSA was first tested on CEC2017 functions. Then MS-TSA was applied to the problems in the CEC2020 competition and compared with the results of the best performing algorithms in this competition. As a result of the comparisons, MS-TSA was found to be a competitive method on solving benchmark functions. Then, parameter estimation of single diode, double diode and photovoltaic module models using the input data of various solar panels was carried out by the MS-TSA. The results obtained with MS-TSA were compared with both the results of the basic TSA and the results of well-known algorithms in the literature. The results obtained are 9.8642E-04, 9.8356E-04, 2.4251E-03, 1.7534E-03 respectively. As a result of the comparative analysis, the lowest RMSE value was obtained by MS-TSA. In addition, comprehensive performance analyzes of the algorithms were made with the convergence curve, boxplots, current (I) - voltage (V) and power (P) - voltage (V) characteristic curves obtained according to the experimental results. As a result of the experiments and analyses, MS-TSA was found to be a more successful method than the compared algorithms in parameter estimation of PV models.

MallobSat: Scalable SAT Solving by Clause Sharing

SAT solving in large distributed environments has previously led to some famous results and to impressive speedups for selected inputs. However, in terms of general-purpose SAT solving, prior approaches still cannot make efficient use of a large number of processors. We aim to address this issue with a complete and systematic overhaul of the distributed solver HordeSat with a focus on its algorithmic building blocks. In particular, we present a communication-efficient approach to clause sharing, careful buffering and filtering of produced clauses, and effective orchestration of state-of-the-art solver backends. In extensive evaluations, our approach named MallobSat significantly outperforms an updated HordeSat, doubling its mean speedup. Our clause sharing results in effective parallelization even if all threads execute identical solver programs that only differ based on which clauses they import at which times. We thus argue that MallobSat is not a portfolio solver with the added bonus of clause sharing but rather a clause-sharing solver where adding some explicit diversification is useful but not essential. We also discuss the last four iterations of the International SAT Competition (2020–2023), where our system ranked very favorably, and identify several previously unsolved competition problems that MallobSat solved successfully. Last but not least, our approach is malleable, i.e., supports running on a fluctuating set of resources, which allows us to combine parallel job processing and parallel SAT solving in a flexible manner for best resource efficiency

A Model-Free Approach for Solving Choice-Based Competitive Facility Location Problems Using Simulation and Submodularity

This paper considers facility location problems in which a firm entering a market seeks to open facilities on a subset of candidate locations so as to maximize its expected market share, assuming that customers choose the available alternative that maximizes a random utility function. We introduce a deterministic equivalent reformulation of this stochastic problem as a maximum covering location problem with an exponential number of demand points, each of which is covered by a different set of candidate locations. Estimating the prevalence of these preference profiles through simulation generalizes a sample average approximation method from the literature and results in a maximum covering location problem of manageable size. To solve it, we develop a partial Benders reformulation in which the contribution to the objective of the least influential preference profiles is aggregated and bounded by submodular cuts. This set of profiles is selected by a knee detection method that seeks to identify the best tradeoff between the fraction of the demand that is retained in the master problem and the size of the model. We develop a theoretical analysis of our approach and show that the solution quality it provides for the original stochastic problem, its computational performance, and the automatic profile-retention strategy it exploits are directly connected to the entropy of the preference profiles in the population. Computational experiments on existing and new benchmark sets indicate that our approach dominates the classical sample average approximation method on large instances of the competitive facility location problem, can outperform the best heuristic method from the literature under the multinomial logit model, and achieves state-of-the-art results under the mixed multinomial logit model. We characterize a broader class of problems, which includes assortment optimization, to which the solving methodology and the analyses developed in this paper can be extended. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research was supported by Fonds de Recherche du Québec-Nature et Technologies and Institut de Valorisation des Données through scholarships to R. Legault. E. Frejinger was partially supported by the Canada Research Chair program [Grant 950-232244]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ijoc.2023.0280 .

Trajectory Design of Potentially Hazardous Asteroid Exploration with Reusable Probes from Cislunar Space

This article presents a trajectory design problem concerning the exploration of potentially hazardous near-Earth asteroids (PHAs) with reusable probes from cislunar space. A total of 20 probes, making round trips departing from and returning to a service space station in a lunar distant retrograde orbit, are expected to explore as many PHAs as possible by means of close flyby within a 10-year time window. The trajectory design problem was released in the 12th edition of China’s Trajectory Optimization Competition on 20 August 2022, and a total of 10 sets of trajectory solutions were submitted. As the authors who proposed the competition problem, we present in this article the problem descriptions, trajectory analysis, and design, as well as an impressive trajectory solution in which a total of 105 PHAs are explored. It is concluded that taking advantage of reusable probes from cislunar space is a promising option to efficiently explore large numbers of PHAs.

Optimal strategies for innovation and market allocation in joint production

Capacity sharing is an important way for relevant enterprises to save production materials. This paper constructs a capacity sharing decision-making model that considers market competition intensity, and introduces the independent market size of participating entities into the model design. This paper explores the impact mechanism of factors such as market competition intensity, independent market size of participating entities, and innovation cost coefficient on system decision-making. Research has found that the increase in independent market size has a consistent impact on the innovation and pricing decisions of enterprises sharing production capacity. Although the increase in independent market size enhances the sharing ability of production line innovation costs, it may not necessarily alleviate the problem of excessive competition among participating entities in overlapping markets. It is also necessary to specifically consider the impact of market competition intensity. The specific research conclusions provide decision-making basis for enterprises to participate in the process of capacity sharing, and also provide theoretical basis for relevant government departments to intervene in the process of capacity sharing.

Competitive pricing for ride-sourcing platforms with MARL

With the advancement of autonomous driving, the competition between shared autonomous vehicles (SAVs) and the existing human-driven vehicles (HVs) ride-sourcing platforms has emerged as a prominent research area within the fields of transportation and management. Solving the pricing problem, considering the competition between HV and SAV platforms, is critical for the platform’s operation. The competitive pricing problem is hard to solve since it is a partially observable Markov decision process (POMDP), that is, only the strategies of other platforms (such as price and wage) are observable, while their states are unobservable. It is also a multivariable sequential problem pricing since spatial-temporal pricing varies with region and time. To tackle this complex problem, this paper implements the multi-agent reinforcement learning (MARL) approach of multi-agent deep deterministic policy gradient (MADDPG) to an mesoscopic simulation system. The simulation replicates the intricate dynamics within a competitive environment where SAV and HV platforms coexist. MADDPG effectively resolves the competitive pricing issue using data derived from simulation. The findings indicate that the SAV platform secures greater profits with a smaller fleet, as it eliminates the need to compensate drivers. Meanwhile, for the same reason, even with fewer vehicles, the SAV platform can set lower prices to attract more passengers and thereby generate higher profits. We further evaluate the efficacy of dynamic pricing versus spatial-temporal pricing and discover that spatial-temporal pricing yields greater profits.

Cost‐effective use of abandoned agricultural land for biofuel production

AbstractBiofuel can be used to abate greenhouse gas emissions in the transport sector, by replacing fossil fuel. To avoid the problem of competition with food production, the use of abandoned agricultural land (AAL) for production of the feedstock for biofuel has been proposed. AAL has generally low productivity but has also low opportunity costs, and production of perennial bioenergy crops on it can lead to carbon sequestration. A spatially explicit optimization model of biofuel production and transport fuel consumption, applied to Sweden, was used for an analysis of how AAL can alter costs for greenhouse gas emissions abatement. Results show that, compared to the case without AAL, AAL could decrease the costs of reducing greenhouse gas emissions by 29%, for emissions reductions equivalent to 50% of current emissions from gasoline in Sweden. The carbon sequestration from establishing perennial bioenergy crops on AAL is the main driver of the positive results. High carbon sequestration on AAL implies larger emissions reduction for a given volume of biofuel, and the results show that the total biofuel production can be both smaller and larger with AAL. The use of arable land for biofuel production is generally smaller with AAL, but larger at some of the highest analyzed target levels. The low AAL feedstock costs contribute to lower costs of the total biofuel production, which pushes for more total biofuel production and less fuel use reduction and therefore counteracts the reduced use of arable land.

The maximal covering location disruption problem

This research sets forth and examines a new sequential, competitive location problem. The maximal covering location disruption problem is a zero-sum Stackelberg game comprised of two stages. A leader denies access to at most q out of n possible facility locations in the first stage and, in the second stage, a follower solves a maximal covering location problem while emplacing at most p facilities. Identifying this problem as both relevant and unaddressed in the current literature, this research examines properties of the bilevel programming formulation to inform heuristic development, subsequently evaluating the efficacy and efficiency of two variants each of an iterative, bounding heuristic (IBH) and a reformulation-based construction heuristic (RCH) over a two sets collectively consisting of 2160 test instances representing a breadth of relative parametric values. Although we illustrate that each heuristic may not identify an optimal solution, computational testing demonstrates the superlative and generally excellent performance of the RCH variants. For the 12.4% of instances for which the RCH does not readily verify the optimality of its solution, lower-bounding procedures characterize solution quality. Both of the RCH variants attain solutions with an average 4.08% relative optimality gap, and they scaled well over different parametric value combinations, solving instances in an average of 98.0 and 123.6 seconds, respectively.

Implementation of a client–centric model of customer service (based on the example of customer service in the field of telecommunications and communications)

Growing competitive pressure and problems of customer churn in the telecommunications industry determine the need for an intensive transition to customer–centric service. The article studies the features of the implementation of a client–centric model of customer service using the example of customer service in the field of telecommunications and communications. The key stages and principles of transition to a customer–centric model are formulated, and the maturity levels of the customer relationship management system are highlighted. Based on the analysis of scientific literature on the research topic, the features and key approaches to managing customer service, development and strategic planning in the activities of telecommunications companies are determined. Objective differences are established in building relationships with B2C and B2B client segments. Based on the results of the study, recommendations are formulated for the implementation of a client–centric model of client service in the field of telecommunications and communications, suggesting a transition to an integrated construction of a client–centric business model for the telecommunications business. It is emphasized that despite the historical tendency towards short–term planning in the telecommunications industry, a customer–centric business model is becoming increasingly in demand in inextricable connection with the ecosystem approach and maximum customer retention.

Global AI governance: barriers and pathways forward

Abstract Recent advances in artificial intelligence (AI), combined with a proliferation in use, have led to a newfound emphasis on strengthening the global governance of AI. In this article, we assess the prospects for stronger global AI governance and consider potential pathways forward. We map the nascent landscape of international regimes focused on AI governance and conclude that a governance deficit remains due to the inadequacy of existing initiatives, gaps in the landscape and difficulties reaching agreement over more appropriate mechanisms. First-order cooperation problems stemming from interstate competition and second-order cooperation problems from dysfunctional international institutions problematize overcoming current deficiencies in global AI governance. In light of these cooperation problems, we evaluate two pathways for strengthening global AI governance: (a) developing new centralized international AI institution(s); and (b) strengthening coordination between, and capacities of, existing institutions. We argue that strengthening the existing weak ‘regime complex’ of international institutions is the more politically legitimate and viable path forward. Improving coordination between, and capacities of, existing international institutions governing AI would support inclusive and mutually reinforcing policy change that can mitigate a range of risks associated with these technologies. As first steps for strengthening the weak AI regime complex, we recommend that the OECD is foregrounded as a centre of expert AI knowledge so that it can facilitate peer pressure among states and harmonization of policies. We also encourage scrutiny of different nodes in the regime complex to ensure that they are fulfilling appropriate functions based on their democratic mandates.

Optimization model for multibeam sounding line deployment based on genetic algorithm

Multibeam sounding technology is a cutting-edge method used for measuring underwater topography, widely applied across various fields. The actual seafloor terrain often exhibits significant fluctuations, leading to challenges such as insufficient coverage in shallow areas and excessive overlap in deep-water zones, resulting in inefficient operations. Rational selection of line spacing helps improve measurement efficiency and ensures data integrity. In this paper, taking the 2023 National College Student Mathematical Modeling Competition Problem B as a background, we conduct a thorough analysis of the variation patterns of the coverage width of multibeam sounding strips with respect to various parameters. We establish models for ocean depth and coverage width, determining how the strip coverage width changes with distance and the angle between survey lines in different scenarios. Finally, employing genetic algorithms and differential methods, we identify decision variables, objective functions, and constraints to formulate a nonlinear programming model. Through iterative searches, we obtain an optimal sounding line deployment plan that maximizes coverage of the target sea area while keeping the overlap rate below 20%. The calculated missed measurement error is approximately 32.774%, and the overlap rate is around 10.477%, indicating a high level of model accuracy.

Political Staff Promotions, Vice Chancellor’s Leadership Style and Performance of Private Universities in Kenya

This study investigated the relationship between political staff promotions, the vice chancellor's leadership style, and the performance of private universities in Kenya, highlighting their significant roles in shaping the educational landscape. Despite the renowned flexibility, autonomy, and competitiveness of private universities, these institutions in Kenya encountered challenges due to political staff promotions and leadership dynamics, potentially impacting their performance and standing in the competitive educational sector. The research was prompted by concerns over the suboptimal performance of private universities in Kenya, with issues such as quality of education, financial constraints, fierce competition, and governance problems being key contributing factors. The study aimed to explore the effects of political staff promotions and the leadership style of vice chancellors on the performance of these institutions, seeking to understand the dynamics influencing their success or shortcomings. Employing Transformational Leadership Theory and Resource Based View Theory as its foundation, the research utilized a descriptive design for an in-depth analysis. Data were collected from a purposively selected sample of five prominent private universities in Kenya through questionnaires and interviews targeting vice chancellors, deans, and lecturers. This methodological approach facilitated a detailed examination of the research variables and their interrelations. The findings indicated that political staff promotions had insignificant impact on university performance, with a non-significant p-value of 0.666 (B = -0.174), suggesting that political considerations, while present, did not substantially affect institutional effectiveness. In contrast, the vice chancellor's leadership style showed a significant positive correlation with university performance, evidenced by a significant p-value of 0.020 (B = 0.475), indicating the role of effective leadership in driving institutional success. This underlined the potential of strong, visionary leadership to guide private universities towards higher levels of excellence and competitiveness. The study recommends a focus on leadership development, the implementation of transparent promotion processes, and efforts to improve teaching quality and infrastructure. By addressing these areas, private universities in Kenya could strengthen their foundations, better navigate existing challenges, and seize opportunities for ongoing improvement and significant contributions to the wider educational sector. Keywords: Leadership style, university performance, political staff promotions, higher education management, private universities in Kenya

Quantum particle swarm optimization algorithm based on diversity migration strategy

Particle swarm optimization algorithm has been successfully applied to solve practical optimization problems due to its simplicity and efficiency. However, the traditional particle swarm optimization algorithm has inferior search performance in complicated high-dimensional optimization issues and is prone to falling into local optima. To address these problems, a new migration mechanism is introduced and a quantum particle swarm optimization method based on diversity migration is proposed. The strategy can capture different ranges of particles in the population, and the selection of migrating individuals depends not only on their fitness values but is also influenced by the positions within the population. The individual with the minimal average Hamming distance in the population can indicate the direction of iterative population optimization. After comparing the fitness values and the average Hamming distance between particles, the particles deviating from the central range of the population are replaced. The performance of the proposed algorithm is investigated under seven different sets of benchmark function optimization problems in the CEC2020 single-objective boundary-constrained optimization competition, and is compared with those of several other representative optimization algorithms. The quantum particle swarm optimization algorithm based on diversity migration strategy outperforms other typical optimization algorithms. Moreover, the proposed algorithm is convergent and stable.