Special Issue (SI): Applications of Optimization Techniques in Engineering Science and Technology

2026-03-12

Short Abstract

https://ojs.wiserpub.com/index.php/EST/

With the main research interests being engineering science and engineering technology, Engineering Science & Technology aims to disseminate the latest scientific theories, research results, and innovative methods among scientists and engineers from engineering disciplines.

The rapid evolution of engineering systems—driven by the integration of intelligent technologies, renewable energy sources, advanced manufacturing, and data-driven decision-making—has created unprecedented challenges that demand sophisticated optimization methodologies. From smart grids and autonomous systems to sustainable infrastructure and industrial processes, engineers face complex problems characterized by multiple objectives, uncertainty, large-scale search spaces, and real-time constraints. Traditional analytical approaches, while valuable, often prove inadequate in addressing the scale and complexity of modern engineering challenges.

Optimization techniques have emerged as indispensable tools for navigating this complexity. Whether through mathematical programming, metaheuristic algorithms, or machine learning-assisted frameworks, optimization enables engineers to identify optimal solutions, enhance system efficiency, and ensure reliability under diverse operating conditions. The convergence of optimization with artificial intelligence, digital twin technologies, and Industry 4.0 paradigms has opened new frontiers for real-time decision-making, adaptive control, and integrated system design.

This Special Issue, entitled "Applications of Optimization Techniques in Engineering Science and Technology," provides a dedicated platform for disseminating cutting-edge research that advances both the theory and practice of engineering optimization. We invite contributions that explore innovative optimization methodologies-including multi-objective optimization, swarm intelligence, evolutionary algorithms, stochastic programming, and hybrid intelligent systems—as well as their practical implementation across diverse application domains.

Topics of interest encompass electrical and power systems, renewable energy integration, smart grids, mechanical design, advanced manufacturing, transportation networks, civil infrastructure, robotics, materials engineering, and sustainable industrial systems. We particularly encourage submissions that demonstrate the synergy between optimization techniques and emerging technologies such as artificial intelligence, digital twins, and real-time decision-support systems.

By bringing together theoretical advancements and real-world case studies, this Special Issue aims to serve as a key reference for researchers, engineers, and industry practitioners seeking to harness the power of optimization for next-generation engineering solutions.

Keywords

Engineering optimization, mathematical programming, metaheuristics, multi-objective optimization, intelligent systems, renewable energy optimization, smart technologies, sustainable engineering, swarm intelligence, evolutionary algorithms, machine learning-assisted optimization, digital twin, real-time decision support, Industry 4.0, smart grids, advanced manufacturing, energy systems, infrastructure optimization, computational intelligence, hybrid intelligent systems.

Research Background and Motivation

Modern engineering systems face unprecedented complexity driven by the integration of intelligent technologies, renewable energy, advanced manufacturing, and data-driven decision-making. These developments demand robust optimization methodologies to enhance efficiency, reliability, and sustainability across diverse applications.

While classical optimization methods have laid important foundations, they often prove inadequate for addressing large-scale, multi-objective, and real-time engineering challenges. This gap has motivated the development of advanced techniques—including metaheuristic algorithms, evolutionary computation, swarm intelligence, and machine learning-assisted optimization—capable of navigating complex problem spaces.

The convergence of optimization with artificial intelligence, digital twins, and Industry 4.0 paradigms presents transformative opportunities for engineering innovation. This Special Issue seeks to consolidate cutting-edge research that bridges theoretical advancements with practical implementations, fostering cross-disciplinary exchange and accelerating the translation of optimization science into real-world engineering solutions.

Necessity and Significance of the Special Issue

As engineering systems grow increasingly complex and interconnected, the demand for advanced optimization techniques has never been greater. From renewable energy integration and smart manufacturing to intelligent infrastructure and real-time decision-making, optimization plays a pivotal role in enhancing efficiency, reliability, and sustainability across diverse engineering domains. Despite significant theoretical advancements, a gap persists between cutting-edge optimization research and its practical implementation in real-world applications. This Special Issue addresses this critical gap by providing a dedicated platform for disseminating innovative research that bridges theory and practice. By bringing together contributions from multiple engineering disciplines, it will foster cross-disciplinary collaboration, accelerate the translation of optimization science into tangible solutions, and serve as an essential reference for researchers and practitioners driving technological innovation.

Forward-Thinking Insights and Research Inspiration

As engineering systems grow increasingly complex and interconnected, optimization continues to evolve beyond traditional boundaries. This Special Issue aims to inspire future research trajectories that will shape the next decade of engineering optimization.

Integration with AI and Machine Learning: The convergence of optimization with machine learning offers transformative potential—from neural network-based surrogate modeling to reinforcement learning for adaptive control in real-time systems.

Digital Twin-Enabled Optimization: Digital twin technologies integrated with optimization frameworks enable dynamic system modeling, predictive maintenance, and continuous performance adaptation, essential for next-generation intelligent systems.

Resilience and Sustainability: Future optimization must embed environmental and social considerations from the outset, addressing lifecycle impacts, circular economy principles, and system resilience against disruptions.

We invite contributions that push boundaries at the intersection of optimization science and engineering innovation, particularly those bridging theoretical advances with practical implementation in real-world systems.

Proposed Research Topics (include, but are not limited to)

Optimization Theory and Foundations
Mathematical programming (linear, nonlinear, integer, and dynamic programming)
Stochastic and robust optimization under uncertainty
Multi-objective and many-objective optimization
Large-scale and distributed optimization algorithms
Metaheuristic and Nature-Inspired Algorithms
Evolutionary algorithms (genetic algorithms, differential evolution)
Swarm intelligence (particle swarm optimization, ant colony optimization)
Hybrid and adaptive metaheuristics
Surrogate-assisted and computationally expensive optimization
Intelligent Systems and Data-Driven Optimization
Machine learning-assisted optimization
Deep learning for surrogate modeling and feature extraction
Reinforcement learning for adaptive control and sequential decision-making
Bayesian optimization and probabilistic methods
Emerging Technologies and Optimization
Digital twin-based optimization for real-time system management
Quantum computing and quantum-inspired optimization algorithms
Blockchain-integrated optimization for decentralized systems
Edge computing and IoT-enabled distributed optimization
Engineering Applications
Electrical and Power Systems
Renewable energy integration and smart grid optimization
Optimal power flow and economic dispatch
Energy storage system sizing, placement, and control
Microgrid planning and real-time energy management
Mechanical and Manufacturing Engineering
Advanced manufacturing process optimization
Product design and topology optimization
Robotics path planning and motion control
Sustainable and Energy Systems
Sustainable energy planning and energy transition modeling
Life cycle assessment and environmentally conscious optimization
Circular economy and waste-to-energy optimization
Transportation and Civil Infrastructure
Traffic flow optimization and intelligent transportation systems
Electric vehicle routing and charging infrastructure planning
Structural design optimization and infrastructure networks
Materials and Chemical Engineering
Computational materials design and discovery
Process systems engineering and chemical process optimization

Guest Editors

Dr. Touqeer Ahmed Jumani

Affiliation: A’Sharqiyah University, Oman

Scopus：https://www.scopus.com/authid/detail.uri?authorId=57203300812

ORCID：https://orcid.org/0000-0002-5201-5111

Email：touqeer.ahmed@asu.edu.om

Timeline

Submission Opens: March 2026

Submission Deadline: December 2026

Editorial Process Timeline

Preliminary Review: 1 week

Peer Review: 4-6 weeks

Final Decision: 2 weeks

Authors are encouraged to submit their manuscripts through the journal's online submission system, adhering to the submission guidelines provided on the journal's website.

Submit Options

- OJS: https://ojs.wiserpub.com/index.php/EST/about/submissions

- Email: est@universalwiser.com

Contact for Inquiries

Dr. Touqeer Ahmed Jumani
A’Sharqiyah University, Oman
Email: touqeer.ahmed@asu.edu.om

We look forward to your contributions to this endeavor!

Michelle Zu

Journal Coordinator

Email: est@universalwiser.com