A Hybrid of Bee Colony Optimization and Genetic Algorithm for Task Allocation in Multi-Core Systems to Minimize Makespan

Hybrid Constraint-Aware Metaheuristic for Energy-Efficient Task Scheduling and Resource Allocation in Cloud Computing with Colored Petri Nets

Fault-Tolerant Task Scheduling in Multicore Systems Using Hybrid Metaheuristic Algorithms

Ensuring reliable and efficient task scheduling remains a critical challenge in multicore computing environments, particularly when system faults can significantly affect performance and interfere with execution. This paper presents a hybrid optimization strategy that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques to improve task allocation under fault-prone conditions. The proposed model considers task dependencies during scheduling and dynamically distributes workloads across available processing cores to achieve balanced utilization while maintaining reliability.To evaluate its effectiveness, the hybrid GA–PSO method was tested against standalone GA and PSO approaches. The experimental findings indicate that the combined strategy achieves shorter execution times, better scalability as workload increases, and a noticeable reduction in task failure rates. These results suggest that integrating evolutionary and swarm-based optimization mechanisms can provide a practical and robust solution for improving both performance and fault tolerance for modern multicore systems.

Green Flexible Job Shop Scheduling Using Genetic Algorithms with Adaptive Neighborhood Search

A Adaptive Genetic Algorithm (AGA) is designed to solve the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions.

Pareto Ant Colony Algorithm Based Task Offloading Optimization for Functional Safety Critical Applications in Fog Computing

Ensuring reliability and real-time performance of task offloading in fog computing remains a critical challenge. To address this, this article considers a dual-objective optimization problem of reliability and execution time for task offloading in energy-constrained fog computing. We first propose a more realistic fog computing system model that incorporates Rayleigh fading. Second, we introduce a reliability and time balanced Pareto ant colony optimization algorithm (RTPACO) based on the Pareto ant colony optimization (PACO algorithm. This algorithm is specifically designed for task offloading scenarios in fog computing. Lastly, we compared RTPACO with other multiobjective optimization algorithms using several metrics, including convergence and diversity (evenness and spread). To evaluate the performance of the algorithms, we employed the widely-used Hypervolume (HV) metric. The experimental results demonstrate that RTPACO consistently achieves a superior Pareto front, with HV improvements ranging from 17.2% to 50.2% compared to existing algorithms.

Hybrid Salp Swarm-Genetic Algorithm Optimization for the Multidimensional Knapsack Problem: A Conceptual Review and Framework Synthesis

The Multidimensional Knapsack Problem (MKP) is a classical NP-hard combinatorial optimization problem used in wide variety of applications such as in logistics, cloud computing, manufacturing, telecommunications, scheduling and resource allocation. Metaheuristic algorithms are widely used because as the size of the problem and complexity of the optimization problem grows, the traditional exact methods are not able to compute them. In this regard, the Genetic Algorithm (GA) and Salp Swarm Algorithm (SSA) have received high interest due to their complementary search capabilities. The global exploration via the adaptive leader–follower mechanism in SSA, and the strong local exploitation by evolutionary operators in GA, complement each other well. In recent years, these algorithms are being incorporated into hybrid frameworks to accelerate the convergence process, preserve the diversity of the population and increase the quality of the solutions in large-scale optimization problems. But most of the current research is implementation oriented, and there are very few conceptual syntheses of concepts for the theoretical foundations, evolution, hybridization strategies and emerging developments of SSA–GA optimization for MKP. This paper discusses the complete overview of the conceptual review of Hybrid Salp Swarm–Genetic Algorithm optimization in Multidimensional Knapsack Problem. It outlines the development of the MKP, metaheuristic optimization, evolutionary computation, swarm intelligence and hybrid optimization and discusses the complementary nature of exploring/exploiting, constraint-handling and adaptive optimization mechanisms. The review also outlines the recent research trends, conceptual gaps, and suggests a common framework to inform the design of the scalable, adaptive, and computationally efficient hybrid optimization models. By consolidating current knowledge and outlining future research directions, this review provides a valuable reference for researchers and practitioners working in combinatorial optimization and intelligent resource allocation.

Study of Decomposition and Ants Allocation in Multi-Objective Partial Classification Rule Induction

This work investigates how balancing the number of decomposed subproblems and ants impacts performance under a fixed computational budget and introduces MOEA/D-AM, a hybrid algorithm that adapts this decomposition strategy for partial classification rule induction.