An adaptive balance optimization algorithm and its engineering application

Although previous studies have explored the formation and effects of dynamic capabilities, much remains to be learned on this topic. There has been little research on the formation and effects of exploitative dynamic capabilities and explorative dynamic capabilities. This paper provides an explanation of how entrepreneurial leadership style and slack resources affect the formation of exploitive dynamic capabilities and explorative dynamic capabilities and evaluates the effects of exploitive dynamic capabilities and explorative dynamic capabilities on competitive advantage. Based on a sample of 382 Chinese firms, the empirical results show that a transactional leadership style and absorbed slack resources can encourage the formation of exploitative dynamic capabilities, and that a transformational leadership style and unabsorbed slack resources are conducive to the development of explorative dynamic capabilities. Furthermore, exploitative dynamic capabilities and explorative dynamic capabilities can reinforce and complement each other. Exploitative dynamic capabilities positively impact explorative dynamic capabilities, and explorative dynamic capabilities enhance exploitative dynamic capabilities. In particular, exploitative dynamic capabilities have an important effect on short-term financial performance, and explorative dynamic capabilities lead to a significant long-term competitive advantage. The results show that explorative dynamic capabilities surpass exploitative dynamic capabilities in terms of competitive advantage, even if both have a positive influence on competitive advantage. This study validates and develops the theory of dynamic capabilities.

Nowadays, there are various optimization problems that exact mathematical methods are not applicable. Metaheuristics are considered as efficient approaches for finding the solutions. Yet there are many real-world problems that consist of different properties. For instance, financial portfolio optimization may contain many dimensions for different sets of assets, which suggests the need of a more adaptive metaheuristic method for tackling such problems. However, few existing metaheuristics can achieve robust performance across these variable problems even though they may obtain impressive results in specific benchmark problems. In this paper, a metaheuristic named the Adaptive Multi-Population Optimization (AMPO) is proposed for continuous optimization. The algorithm hybridizes yet modifies several useful operations like mutation and memory retention from evolutionary algorithms and swarm intelligence (SI) techniques in a multi-population manner. Furthermore, the diverse control on multiple populations, solution cloning and reset operation are designed. Compared with other metaheuristics, the AMPO can attain an adaptive balance between the capabilities of exploration and exploitation for various optimization problems. To demonstrate its effectiveness, the AMPO is evaluated on 28 well-known benchmark functions. Also, the parameter sensitivity analysis and search behavior study are conducted. Finally, the AMPO is validated on its applicability through a portfolio optimization problem as a challenging example of real-world applications. The benchmark results show that the AMPO achieves a better performance than those of nine state-of-the-art metaheuristics including the IEEE CEC winning algorithms, recent SI and multi-population/hybrid metaheuristics. Besides, the AMPO can consistently produce a good performance in portfolio optimization.

Address wind farm layout problems by an adaptive Moth-flame Optimization Algorithm

Stochastic particle swarm optimization is a novel variant of particle swarm optimization that convergent to the global optimum with probability one. However, the local search capability is not always well in some cases, therefore, in this paper, a technique, dynamic step length, is incorporated into the structure of stochastic particle swarm optimization aiming to further improve the performance. In this modification, each particle will adjust its velocity according to its performance. In other words, if it finds a better region, it will make a local search, otherwise, a global search pattern is given. By the way, to combining the advantages between the standard version (with better exploitation capability) and the stochastic version (with better exploration capability), the first half period is used with the standard version incorporated with dynamic step length, while in later generations, the stochastic version with dynamic step length is used to escape from a local optimum. Simulation results show this strategy may provide well balance between exploration and exploitation capabilities, and improve the performance significantly.

PurposeThe purpose of this paper is to enrich the explanation of the interplay between internal and external – or district shared – exploration and exploitation capabilities as antecedents of a firm’s radical and incremental innovation. Previous studies do not differentiate between exploration and exploitation in district shared capabilities and how they interact with internal capabilities.Design/methodology/approachThe paper uses hierarchical regression analysis to test the quadratic and moderating effects in a sample of 1,019 Spanish firms.FindingsResults show an increasingly positive effect on radical innovation of exploration capabilities, enhanced by shared capabilities in exploration. In the case of incremental innovation, the study finds evidence of an increasingly positive influence of exploitation capabilities and a concave relationship of exploration capabilities. Moreover, shared exploitation capabilities weaken the effect of internal exploitation capabilities and also have a direct effect on incremental innovation. Therefore, the two capabilities are interchangeable in the effect they have on incremental innovation.Practical implicationsDepending on the firm’s innovation strategy, intra-district firms should develop specific capabilities and/or concentrate on adopting the shared capabilities in the destination.Originality/valueThe study furthers the understanding of the relationship between exploration and radical innovation, and between exploitation and incremental innovation, which is more complex than previously depicted. The study also differentiates between exploration and exploitation in shared capabilities, enriching understanding of the competitiveness of district firms.

Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning

Studies focusing on coopetition and dynamic capabilities have expanded significantly over the past several decades. Coopetition strategy and dynamic capabilities are increasingly recognised as sources of sustained competitive advantage. The purpose of this paper is to provide a better understanding of the factors driving growth performance in digital healthcare ventures by examining the role of coopetition, exploration and exploitation capabilities, and environmental uncertainty. While numerous studies have examined the competitive advantage of coopetition, its specific contribution to the growth of ventures in the digital realm remains less explored. Clarifying the strategic role of coopetition in driving growth performance is critical for delineating the intricate relationship between coopetition and growth performance, particularly in the context of digital healthcare ventures. To fill in this research gap, this study uses coopetition theory and dynamic capabilities theory to look at how exploration and exploitation capabilities, as well as environmental uncertainty, affect the relationship between coopetition and growth performance in digital healthcare ventures. We collected a total of 338 questionnaires from Chinese digital healthcare ventures between March 2023 and August 2023. We conducted data analysis using SPSS 26.0 and its macro-program PROCESS. Our results confirm that coopetition has a positive effect on growth performance in digital healthcare ventures. Furthermore, exploration and exploitation capabilities fully mediate the relationship between coopetition and growth performance. Moreover, environmental uncertainty significantly and distinctively moderates the impact of exploration and exploitation capabilities on growth performance. This study contributes to the existing literature by providing deeper insight into the relationship between coopetition and growth performance in digital healthcare ventures. It also offers important practical implications for public health improvement and socio-economic development.

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

In this dissertation, three different evolutionary algorithms are proposed by hybridizing the Taguchi method, chaos disturbance method, multilevel immune algorithm (MIA), clonal selection algorithm (CLONALG), and artificial bee colony algorithm (ABC). Including multi-level immune algorithm and clonal selection algorithm is derived by the immune algorithm. These algorithms are thus called HTCMIABC, HTCABC, and HCABC. The HTCMIABC comprises two main different phases. We use the MIA as the recognition phase to balance local and global search and accelerate the search speed to enhance the evolutionary phase. Second, the evolutionary phase is built on the ABC and chaos disturbance operation to have the capabilities of exploration and exploitation. Moreover, the Taguchi method and crossover operation are inserted between the recognition phase and evolutionary phase for the recombination and diversification of several antibodies to improve the searching ability. In the HTCABC, the chaos search algorithm and adaptive bound method are adopted to improve the ABC performance. Then, the Taguchi method and crossover operation are incorporated into the chaos artificial bee colony (CABC) to accelerate the search capacity. Moreover, the natural phenomenon of the elite strategy is adopted and the recruitment of new scout bees is used for HTCABC, which can maintain the diversity of the population, and escape from local optima. Additionally, there is no complex parameter setting in the algorithm design. In the HCABC, the mutation mechanism of the CLONALG by using the advantages of ABC can improve the capabilities of exploration and exploitation. Finally, the HTCMIABC algorithm is examined by parameter identification of nonlinear chaotic system. Simulation results show that the HTCMIABC is more efficient than some typical existing algorithms. The HTCABC algorithm is examined by using a set of benchmarks and the proposed approach is also applied to solve the parameter identification of a chaotic system. Simulation results show that the HTCABC is more efficient than some existing algorithms reported in the literature. In addition, the simulation results also demonstrate that HCABC can effectively achieve the best PID-like fuzzy controller structure and parameters.

ABSTRACTDrawing upon organizational information processing theory, we investigate how explorative and exploitative information technology (IT) capabilities independently and interdependently moderate the relationship between supply chain information integration and firm performance. Data collected from 215 firms in China reveal that explorative IT capability positively moderates the relationship between information sharing and firm performance but exerts no moderating effect on the association between collaborative planning and firm performance. By contrast, exploitative IT capability positively moderates the impact of collaborative planning but negatively moderates the influence of information sharing on firm performance. Furthermore, building upon the ambidexterity perspective, we develop and test a three‐way interaction hypothesis that explorative and exploitative IT capabilities would interdependently moderate the relationship between supply chain information integration and firm performance. Our results indicate that explorative and exploitative IT capabilities are complementory in moderating the link between collaborative planning and firm performance but substitutive in moderating the relationship between information sharing and firm performance. By integrating insights from both information systems and operations management, our study thus provides an in‐depth understanding on how and when IT business value can be created in supply chain information integration context.

A contingency view of the effects of sustainable shipping exploitation and exploration on business performance

Whether performance advantage accrues more to firms that balance exploitation (i.e., serving existing markets) and exploration (i.e., developing new products for future markets) or to those that focus on only one of these objectives is open to debate. To gain a better understanding of this dilemma, we employ longitudinal data from the pharmaceutical industry to examine how efficient firms are at utilizing resource investments to build exploitation and exploration capabilities. These capabilities are obtained using stochastic frontier estimation, which measures how efficiently firms use resource inputs to generate an output in a competitive landscape. We then link these capabilities to performance. We find that firms with superior exploitation capabilities have higher operating cash flow but lower firm value. In contrast, firms with superior exploration capabilities find the opposite effect. Importantly, firms that are superior in both capabilities enhance cash flow but not firm value.

This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem very efficiently with optimality guarantee. From the perspective of dynamical systems, a heuristic local search algorithm for an NP-complete combinatorial problem is a discrete dynamical system. In a local search system, an attractor drives the search trajectories into the vicinity of a globally optimal point in the solution space, and the convergence of local search trajectories makes the search system become a global and deterministic system. The attractor contains a small set of the most promising solutions to the problem. The attractor can reduce the problem size exponentially, and thus make the exhaustive search feasible. Therefore, this new search paradigm is called optimizing with attractor. The ABSS consists of two search phases: local search phase and exhaustive search phase. The local search process is used to quickly construct the attractor in the solution space, and the exhaustive search process is used to completely search the attractor to identify the optimal solution. Therefore, the exact optimal solution can be found quickly by combining local search and exhaustive search. This tutorial introduces the concept of an attractor in a local search system, and describes the process of optimizing with the attractor, using the Traveling Salesman Problem as the study platform.

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator’s objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer–Rao lower bound (CRLB).

Recent studies have addressed the significance of the HR system concerning organization ambidexterity and firm innovation performance, however, research overlooked the influential power of intellectual capital-enhancing HR configurations both on firm innovation performance and ambidexterity. We designed rigorous time-lagged research with three waves of data gathering. We have found that firm explorative and exploratory capabilities significantly mediate the relationship between human, social and organizational capital-enhancing HR configurations and firm innovation performance. In fact, our study mirrors that how such a specific form of HR system has the capacity to fuel firm ambidexterity toward innovation performance and answers the ongoing debates on firms’ contradictory demand for exploitative and exploratory capabilities. By well-articulating the importance of IC-enhancing HR practices and by identifying the mediation effect of firm exploitative and exploratory capabilities in linking IC-enhancing HR practices to firm innovation performance, this study extends prior research and contributes to the field of HRM and ambidexterity. We also provide a synthesis of future research suggestions.