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The challenges posed by structured large-scale tasks to resource-sensitive intelligent transportation systems have been acknowledged, particularly regarding the need to reduce delay and energy consumption during the caching and offloading ...

In the sphere of managing multiple, conflicting objectives concurrently, non-dominated sorting emerges as a pivotal method guiding decision-making towards optimal solutions by generating one or more Fronts. While numerous algorithms exist for ...$$$$${}^{\mathrm{}}$

With the proliferation of structured applications in intelligent transportation systems, cloud-edge-end collaboration technology has gained widespread attention. In order to reduce the offloading delay and energy consumption of structured ...

Industry 5.0 makes it imperative to reevaluate the manner of using resources in manufacturing systems to ensure sustainability. In this context, scheduling problems are encountering new environmental and human-related challenges, and the concept ...

• Human-centric manufacturing flexible job shop integrated.
• Energy consumption considered.
• Economic, environmental and social aspects balanced.
• Q-Learning based improved NSGA-III developed.
• Workers safety and preferences: ...

The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is the most prominent multi-objective evolutionary algorithm for real-world applications. While it performs evidently well on bi-objective optimization problems, empirical studies suggest that it ...

We consider the problem of design of granular materials for optimal gripping behaviour in soft robotic devices. This represents a highly challenging multi-dimensional design problem, requiring consideration of material, geometry and control strategies. ...

Efficient management of berth allocation (BAP), quay crane allocation (QCAP) and transshipment operations is crucial for the optimization of container terminals. However, there is limited research focusing on BAP in the context of ship-to-ship ...

This paper introduces a hybrid intelligent framework that combines Bayesian optimization (BO), a random forest (RF) model, and the nondominated sorting genetic algorithm-III (NSGA-III) for the optimization and control of tunnel shield ...

This proposed study deals with a typical dual-objective multi-task simultaneous supervision dual resource constrained (MTSSDRC) problem to optimize total tardiness and labor productivity. The problem is modeled mathematically into mixed-integer ...

• The scheduling problem is adjusted into mixed-integer quadratic programming.
• Two objectives are considered, total tardiness and labor productivity.
• A solver is limited only for small and medium problems.
• Metaheuristics using ...

With the growing interest in win–win cooperation within the logistics industry recently, collaborative multicenter logistics networks have witnessed the establishment of numerous alliances aimed at achieving cost savings. However, alliance ...

Multiobjective/Many-objective Optimization Genetic Algorithm (MOGA) is a method that utilizes a stochastic search inspired by nature to find solutions in a multiobjective optimization framework. Examples of MOGA include NSGA-II and NSGA-III, both ...

Wireless Sensor Networks have an event-driven nature that includes a colossal count of sensors to generate unpredictable network traffic that causes network congestion Due to immoderate congestion packets remain in the queue for a longer period. ...

Feature selection (FS) is an indispensable activity in machine learning, whose purpose is to identify relevant predictive values from a high-dimensional feature space to improve performance and reduce model learning time. However, the large ...

Increasing optimizing criteria in modern power systems promotes the birth of many-objective optimal power flow (Ma-OPF) problems in which more than three optimizing objective functions are considered. The increasing optimizing ...

• Integrating an eliminating strategy into NSGA-III to solve many-objective OPF problem.

This paper presents a nondominated sorting genetic algorithm III with three crossover strategies (NSGA-III-TCS) for the combined heat and power dynamic economic emission dispatch (CHPDEED) problems with or without prohibited operating zones. The ...

• Combined heat and power dynamic economic emission dispatch (CHPDEED) is studied.
• A NSGA-III with three crossover strategies (NSGA-III-TCS) is presented for CHPDEED.
• The Pareto sets of the NSGA-III-TCS have high hypervolumes and ...

Hybrid energy storage system (HESS), a combination of lithium battery (LIB) and supercapacitor (SC) in hybrid electric vehicles (HEV), can slow down the degradation of battery performance and improve the peak current characteristics by using an ...

• Fuzzy logic energy management applied to HESS in PHEV.
• Modifications to the multi-objective algorithm NSGA-III.
• 4-objective EMS optimization is proposed to optimize FLC parameters.
• Experiments were conducted to validate in four ...

In this work, we present a mixed-integer programming model for a multi-objective home healthcare delivery problem. The proposed problem is modeled with minimum assumptions about the procedure attributes and can handle most of the ...

The series of non-dominated sorting based genetic algorithms (NSGA-series) has clearly shown their niche in solving multi- and many-objective optimization problems since mid-nineties. Of them, NSGA-III was designed to solve problems having three ...

Real-world multiobjective optimization problems involve decision makers interested in a subset of solutions that meet their preferences. Decomposition-based multiobjective evolutionary algorithms (or MOEAs) have gained the research community’s ...

Fog computing aims to provide resources to cloud data centers at the network’s edge to support time-critical Internet of Things (IoT) applications with low-latency requirements. Protecting the IoT-Fog resources and the scheduling ...