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All Journal Buletin Ilmiah Sarjana Teknik Elektro Control Systems and Optimization Letters
Zhang, Jincheng
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Control Systems and Optimization Letters

 1 
The Aries Metaheuristic Algorithm: Exploring Global Optimization Through Impulse, Passion, and Adventure Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.210

Abstract

As optimization algorithms are increasingly used in various fields, metaheuristic algorithms have become a research hotspot due to their powerful global optimization capabilities. Inspired by Aries's adventurous spirit, passion, and motivation, this paper proposes a new metaheuristic algorithm, the Aries metaheuristic algorithm (AMA), which aims to optimize the objective function in multidimensional complex problems. This paper elaborates on the design concept, algorithm flow, and characteristics of AMA, and demonstrates the advantages of AMA in global search through experimental verification on classic benchmark functions and practical problems. Finally, compared with traditional algorithms such as particle swarm optimization (PSO), differential evolution (DE), simulated annealing (SA), and random search (Random), AMA has been shown to have superior performance in solving optimization problems. The core innovation of AMA lies in its impulsive search, emotion-driven jumping, and collective cooperation mechanisms, which simulate Aries-like psychological dynamics to guide the global optimization process.
Classical Dance-Metaheuristic: A Metaheuristic Optimization Algorithm Inspired by Classical Dance Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.206

Abstract

This paper proposes a metaheuristic optimization algorithm based on classical dance, namely the Classical Dance Metaheuristic (CDMH). The algorithm combines the core elements of ballet, Indian classical dance and Chinese classical dance with modern optimization techniques, providing a new approach to high-dimensional optimization problems. The CDMH algorithm optimizes the search process through three stages of simulation: the posture training stage in bllaet, the rhythm and mudra exploration stage in Indian classical dance, and the integration stage of body, rhythm and artistic conception in Chinese classical dance. Experimental results show that CDMH shows good optimization ability in multiple classic optimization problems and can effectively avoid the dilemma of local optimal solutions.
Duck Foraging Algorithm (DFA): A Metaheuristic Algorithm Inspired by Duck Foraging Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 2 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i2.215

Abstract

For many complex optimization problems, the solution process often involves exploring multidimensional space, balancing global and local solutions, and improving the efficiency of the algorithm. In order to improve the optimization efficiency, this paper proposes a new metaheuristic algorithm called the Duck Foraging Algorithm (DFA). The algorithm is inspired by the behavior patterns of wild ducks in nature when foraging, especially their intra-group cooperation, clear division of labor, territoriality, and mobile foraging strategies. By simulating the foraging behavior of ducks, DFA can effectively explore and develop complex solution spaces and find the global optimal solution. The core principles and processes of the algorithm are elaborated in detail and compared with existing optimization algorithms. Finally, we verify its superiority in different types of optimization problems through a series of numerical experiments. Compared with traditional algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC), DFA incorporates unique behavioral mechanisms—such as dynamic leadership switching and decentralized area foraging—based on duck group strategies. In particular, the leader duck guides the group based on fitness ranking, while other ducks balance local search and migration, reflecting a cooperative yet diversified exploration strategy.
Pheasant Foraging Algorithm (PFA): A Bio-Inspired Approach for High-Dimensional Optimization Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.216

Abstract

This paper proposes an optimization algorithm based on pheasant foraging behavior, the Pheasant Foraging Algorithm (PFA). The algorithm simulates the collective cooperation and strategy selection of pheasant groups in the foraging process and is used to solve high-dimensional optimization problems. Based on the analysis of pheasant foraging patterns, an adaptive improvement strategy is proposed to improve local search efficiency while maintaining global search capabilities. Experimental results show that compared with classical optimization methods such as particle swarm optimization (PSO) and genetic algorithm (GA), the PFA algorithm has better performance on many standard optimization problems, stronger global search capabilities and more stable convergence performance. The core innovation of PFA lies in its adaptive improvement strategy, which dynamically adjusts search behavior based on environmental feedback to balance global exploration and local exploitation. Unlike PSO and GA, which often suffer from premature convergence or limited local refinement, PFA introduces role-based cooperation and adaptive flight mechanisms inspired by pheasant group foraging behavior. 
MayNet: A Neural Network Ensemble Approach Based on May's Theorem for Improved Classification Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.213

Abstract

In this study, we explored the possibility of applying May's Theorem to neural networks and proposed a new unified network architecture called MayNet. MayNet achieves category prediction by integrating multiple neural network "voters" and uses majority voting to determine the final classification result. Experimental results show that MayNet outperforms traditional single neural networks on CIFAR-10 and MedMNIST datasets and has high robustness. The paper compares the performance of MayNet with popular convolutional neural networks (such as ResNet18) on various datasets and demonstrates its superior performance. May's Theorem provides a solid theoretical foundation for the majority voting mechanism in neural network ensembles, ensuring improved decision accuracy through collective judgments of independent voters. MayNet’s architecture innovatively integrates multiple independently trained convolutional neural networks as voters, leveraging majority voting to combine their outputs effectively. This design enhances classification accuracy, robustness, and generalization ability.
A Novel Incentive-Compatible Neural Network Optimization Model (ICNNOM) with Optimal Contract Structure Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.212

Abstract

In this paper, we propose a novel neural network optimization framework called the Incentive Compatible Neural Network Optimization Model (ICNNOM). This model combines the incentive compatibility idea in game theory with the optimal contract theory to simulate the "incentive and effort" mechanism between the internal layers of a deep neural network, aiming to improve the learning effect of the network. This paper uses two sets of codes with different architectures to conduct experiments on the CIFAR-10 and CIFAR-100 datasets and compares them with traditional neural network models. The results show that ICNNOM outperforms traditional models in multiple evaluation indicators such as accuracy, precision, recall, and F1 value, proving the effectiveness of introducing incentive mechanisms for model optimization. Incentive compatibility (IC) refers to designing mechanisms so that each participant's best interest aligns with truthful or cooperative behavior, while optimal contract theory studies designing agreements to maximize benefits under informational asymmetry. By integrating these concepts, ICNNOM explicitly coordinates the effort of each neural network layer to improve overall training consistency and efficiency.
Human Breakfast Selection Algorithm (HBSA): A Human-Inspired Metaheuristic for Constrained Optimization Zhang, Jincheng; Zhang, Jindong
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.214

Abstract

In this paper, we propose a new metaheuristic algorithm inspired by human daily breakfast choice behavior, namely the human breakfast choice algorithm (HBSA). When deciding what to eat for breakfast, people often consider multiple goals, constraints, and personal preferences. The algorithm simulates the memory mechanism, preference guidance, contextual adaptation, and hybrid decision-making strategies of human breakfast choices to achieve more effective exploration capabilities in solving combinatorial optimization problems. We apply the algorithm to a typical 0-1 knapsack problem and conduct comparative experiments with genetic algorithms (GA) and particle swarm optimization algorithms (PSO). The results show that the improved HBSA performs better in terms of solution quality and stability.
Co-Authors Jantakoon, Thada Laoha, Rukthin Limpinan, Potsirin Zhang, Jindong