APPLICATION OF QUADRATIC PROGRAMMING ON PORTFOLIO OPTIMIZATION USING WOLFE’S METHOD AND PARTICLE SWARM OPTIMIZATION ALGORITHM

This study aims to analyze the influence of competence, promotion, and job transfer on employee performance at PT Astra International Tbk – Toyota Sales Operation (Auto2000). The research is grounded in the importance of human resource management as a key factor in supporting organizational success. High competence enables employees to perform their tasks optimally, while fair promotions and well-planned job transfers can enhance motivation and broaden work experience. This research employs a quantitative approach using a survey method, with questionnaires distributed to 126 Auto2000 Service Managers as respondents. The analytical technique used is multiple linear regression to determine the effect of each independent variable on the dependent variable. The results indicate that competence, promotion, and job transfer have a positive and significant effect on employee performance, both partially and simultaneously. The findings offer practical recommendations for Auto2000 management to strengthen ongoing training and competency development programs, particularly in aspects of self-efficacy and role understanding. Furthermore, promotions should be based on actual performance and potential, rather than merely on tenure. Job transfers should also be accompanied by appropriate preparation and assessment of employee readiness to ensure effective adaptation in the new position. Through this strategy, the company can create a conducive work environment and sustainably improve employee loyalty and productivity.

Micro, Small and Medium Enterprises (MSMEs) in Indonesia, which are contributors to national economic growth, still experience many obstacles in their development, such as ineffective partnership patterns, and their development does not meet expectations. This makes the implementation of CSR carried out by companies or BUMN still not on target or still partial. The aim of this research is to evaluate and create a pattern of synergy between the implementation of corporate CSR and community economic empowerment in the form of MSMEs. This research was conducted at PT Astra International Tbk with one of the CSR partners in the form of Kampung Berseri Astra, namely KBA Semanggi Suroboyo. The research method is a qualitative approach, using 3 key informants. The results of the research show that PT Astra International Tbk has committed to implementing corporate CSR with a focus on community economic development through Kampung Berseri Astra which is developed and monitored continuously every year through 4 main pillars, namely Education, Health, Environment and Entrepreneurship. KBA Semanggi Suroboyo, as one of the partners, feels that the implementation of CSR is right on target, but is still hampered by the synergy of regional government programs, especially those related to empowering MSMEs. An appropriate synergy pattern is needed to synergize the implementation of the Company's CSR with community economic empowerment, especially in terms of empowering MSMEs.

This research examines the financial performance of PT Astra International Tbk during the period 2020-2023 through comprehensive profitability ratio analysis. The study employs multiple financial metrics including Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), Gross Profit Margin (GPM), and Operating Profit Margin (OPM) to evaluate the company's operational efficiency and profitability. Through quantitative analysis with a descriptive approach, this study reveals that PT Astra International demonstrated resilient financial performance despite challenging market conditions. The findings indicate average ROA of 8.05%, suggesting moderate asset utilization efficiency. The company maintained a healthy ROE averaging 13.92%, though below industry benchmarks, while achieving a stable GPM of 22.6% and OPM of 15.06%, reflecting effective cost management and operational efficiency. These results provide valuable insights for stakeholders and contribute to understanding the automotive sector's financial dynamics in Indonesia.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

This study aims to analyze the financial performance of major companies in Indonesia using the Economic Value Added (EVA) approach as an indicator of value creation. The companies examined include PT Astra International Tbk (ASII), PT Bank Central Asia Tbk (BBCA), PT Bank Negara Indonesia (Persero) Tbk (BBNI), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), PT Bank Mandiri (Persero) Tbk (BMRI), PT Media Nusantara Citra Tbk (MNCN), PT Bukit Asam Tbk (PTBA), and PT Aneka Tambang Tbk (ANTM). EVA is calculated using data on Net Operating Profit After Tax (NOPAT), Invested Capital (IC), and the Weighted Average Cost of Capital (WACC). The results show that most companies generated positive EVA, creating shareholder value. BBCA, BBRI, and BMRI recorded the highest EVA, reflecting effective capital utilization and sound business strategies. On the other hand, ANTM posted a negative EVA, suggesting that its capital cost exceeded its net operating profit. These findings offer valuable insights for investors and corporate management in assessing financial efficiency and making strategic decisions focused on long-term value creation.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

In order to solve the problem of interaction between multiple evaluation indexes of seed metering performance under multiple factors of centralized seed feeding device, a multi-objective optimization of structure based on particle swarm optimization (PSO) algorithm was proposed in this paper. The wheat centralized seed feeding device was taken as the research object, and the experimental factors were cone angle of type hole, working speed and seed filling gap. The working process of wheat centralized seed feeding device was simulated by discrete element method (DEM). The average seed number of type hole, the variation coefficient of the average seed number of type hole, and the maximum tangential force between seed and seed feeding mechanism were selected as the evaluation indexes. Through the variance analysis of the evaluation indexes by the experimental factors, the optimization objective function was constructed. Using PSO algorithm, the multi-objective optimization was carried out for the wheat centralized seed feeding device. The optimization results show that the best structural combination parameters of the wheat centralized seed feeding device are the hole cone angle of 31.6° and the seed filling gap of 4.6 mm. The validity of the method was verified by simulation and field test. The results show that the PSO algorithm multi-objective optimization method proposed in this paper can provide a reference for the structural improvement and optimal design of the centralized seed feeding device. Keywords: centralized seed feeding device, multi-objective, optimization, PSO algorithm DOI: 10.25165/j.ijabe.20201306.5665 Citation: Wang Q Q, Li Z D, Wang W W, Zhang C L, Chen L Q, Wan L. Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm. Int J Agric & Biol Eng, 2020; 13(6): 76–84.

The purpose of this study was to determine and analyze the most dominant factors influencing consumer behavior to buy Toyota Avanza cars (case study at PT Astra International Tbk Auto 2000 Medan). The benefits of research for the development of science, especially in the field of consumer behavior, as a reference in conducting further research. The number of samples is 100 respondents, the types of data used are primary and secondary data, and the method of collecting data through questionnaires and documentation, the method of data analysis is used factor analysis.
 From the results of research and discussion obtained the KMO Measure of Sampling Adeguacy (MSA) Figures of 0797 with a significance level of 0,000. Because the KMO value is above 0.50 and is significant at 1 percent, the data are quite feasible for further analysis using factor analysis. Component Matrix shows that the variables analyzed are not clearly included in which factors. After the rotated component matrix is performed, the twenty variables studied are clearly included in factor 1, factor 2, factor 3, factor 4 and factor 5. The dominant factors affecting consumer behavior to buy Toyota avanza in PT Astra International Auto 2000 Medan are car prices, car brands, lifestyle, income levels and experience. The most dominant factor influencing consumer behavior is the price of a car. Suggestions that are recommended should the company target and intensify marketing in the community who work as entrepreneurs. This is based on the work of entrepreneurs who demand high mobility and have strong capital in conducting transactions, especially car purchases. Affordable and rational prices need to be considered by the company. Companies can also make the payment system look lighter by collaborating with finance companies that have a good track record.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

The maximum power point tracking (MPPT) system ensures that the solar cell operates at its maximum power point, which is the point at which it produces the most power for a given set of conditions. This study uses an artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithms to optimize the boost converter. The ABC and PSO algorithms help to optimize the boost converter by finding the best possible duty cycle and other parameters that maximize efficiency. The resulting duty cycle of 20% produced the highest efficiency of 78.26% obtained from the boost converter. A system-wide test was conducted between 9:00 and 11:10 WIB. In MPPT systems using boost converters with the PSO algorithm, the average power is 16.80 W, while in MPPT systems using boost converters with the ABC algorithm, the average power is 14.53 W. The novelty of the study discusses a system designed to optimize power by using a boost converter in order to increase power output from solar panels. The algorithms PSO and ABC are used to analyze the data from the PV and adjust the converter parameters to optimize the output in order to increase the efficiency of the system.

In order to improve the stability and sensitivity of particle swarm optimization (PSO) algorithm and to solve the problem of premature convergence, in this re-search, a computer PSO algorithm based on thermodynamic motion mechanism is proposed based on the principle of thermodynamic motion mechanism. Firstly, the thermodynamic motion phenomenon, the diffusion law in kinematics and the standard PSO algorithm are introduced. Then, according to the basic idea of thermodynamic motion mechanism, the standardized PSO algorithm is optimized and its optimization process is introduced. Finally, the experimental results are analysed by setting the test function. The results show that among the five test functions, the computer PSO algorithm based on thermodynamic motion mechanism has a higher probability of jumping out of the local optimal solution. Its robustness and stability are much better than standard PSO algorithms. The evolution ability of the computer PSO algorithm based on thermodynamic motion mechanism is better than that of the standard PSO algorithm. The standard PSO algorithm is superior because it is based on thermodynamic motion mechanism. The research in this paper can provide good guidance for improving the performance of PSO algorithm.

In this paper, we present a concept of a transistor level implementation of the Particle Swarm Optimization (PSO) algorithm that belongs to the group of unsupervised learning algorithms aimed at the design of artificial neural networks (ANNs). The algorithm exhibits an ability to search for an optimal solution in a multidimensional data space, in which many sub-optimal solutions may exist. The ANN that operates in accordance with the PSO algorithm is composed of a set of cooperating particles (agents) that explore an input data space and communicate information on the best found solution to other particles. The PSO algorithm is usually implemented in software. We in our investigations focus on its transistor level realization. Such an approach enables parallel data processing, in which the overall data rate only moderately depends on the number of particles. Most of the operations and components of such implemented PSO algorithm may be reused considering our former CMOS realizations of other self-organizing learning algorithms. This allowed us to assess main parameters of the PSO.