An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant

This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

Eyes open and hands on: market knowledge and marketing capabilities in export markets

PurposeDrawing on the knowledge-based view of the firm (KBV) and Dynamic Marketing Capabilities (DMC), this paper examines the role of key internationalization knowledge absorption processes as learning strategies, namely market exploitation and market exploration in enabling internationalization knowledge absorption in export-oriented firms involved in manufacturing goods or producing electrical/engineering products.Design/methodology/approachThe data were gathered via a cross-sectional survey using a questionnaire (i.e. n = 315) on a sample of Bangladeshi manufacturing firms exporting in US and European markets.FindingsThe findings suggest that an export firm's internationalization absorption strategies are positively associated with export performance. The authors also found that the mediator, DMC, strengthened the relationship between knowledge absorption and export performance. Moreover, the findings of moderated mediation model revealed that the direct and indirect effects of market exploitation on export performance are more prevalent when competitive intensity is low. While competitive intensity is high, the direct and indirect effects of market exploration on export performance are more prevalent.Practical implicationsBy introducing a higher-level dynamic marketing capability approach and linking it to ambidexterity constructs (learning though exploration and exploitation), export business professionals should appreciate the full spectrum of mid-level marketing capabilities they need to develop alongside their exploration and exploitation strategies to improve their export performance. This study directs attention to the competitive intensity conditions the exporting firm is facing. When export business professionals are faced with high-level of competitive intensity in the market, they should establish a clear focus on their exploration learning strategies if they wish to enhance their export performance.Originality/valueThe authors contribute to two broad domains of literature: organizational learning and DMC strategy. The study results show that how the two components of international ambidexterity as organizational learning constructs (i.e. market exploration and exploitation) influence knowledge management processes within firms through a firm's possession of a fine configuration of higher-level marketing capability. This study also theoretically and empirically examines how higher-level DMC strategy can mediate the consequence of international knowledge absorption mechanism on firm export performance. From a practical perspective, this study provides useful lessons for exporting firms wishing to enhance their performance.

Logistic Map and Exponential Scaling Factor based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

A new hybrid algorithm based on MVO and SA for function optimization

Hybrid algorithms are widely used today to increase the performance of existing algorithms. In this paper, a new hybrid algorithm called IMVOSA that is based on multi-verse optimizer (MVO) and simulated annealing (SA) is used. In this model, a new method called the black hole selection (BHS) is proposed, in which exploration and exploitation can be increased. In the BHS method, the acceptance probability feature of the SA algorithm is used to increase exploitation by searching for the best regions found by the MVO algorithm. The proposed IMVOSA algorithm has been tested on 50 benchmark functions. The performance of IMVOSA has been compared with other latest and well-known metaheuristic algorithms. The consequences show that IMVOSA produces highly successful and competitive results.

DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm

<abstract> <p>The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, and easy to fall into local optimum. This paper, an improved SMA based on dominant swarm with adaptive t-distribution mutation (DTSMA) is proposed. In DTSMA, the dominant swarm is used improved the SMA's convergence speed, and the adaptive t-distribution mutation balances is used enhanced the exploration and exploitation ability. In addition, a new exploitation mechanism is hybridized to increase the diversity of populations. The performances of DTSMA are verified on CEC2019 functions and eight engineering design problems. The results show that for the CEC2019 functions, the DTSMA performances are best; for the engineering problems, DTSMA obtains better results than SMA and many algorithms in the literature when the constraints are satisfied. Furthermore, DTSMA is used to solve the inverse kinematics problem for a 7-DOF robot manipulator. The overall results show that DTSMA has a strong optimization ability. Therefore, the DTSMA is a promising metaheuristic optimization for global optimization problems.</p> </abstract>