A Novel Adaptive Artificial Bee Colony Algorithm for Multi-Objective UFLP Problems

Economic Emission Dispatch Employing a Novel Improved Multi-Objective Artificial Lemming Algorithm

Multi-objective optimization algorithms are essential for solving complex engineering problems. However, conventional approaches often struggle with limited convergence accuracy and poor solution diversity. This paper proposes the Improved Multi-objective Artificial Lemming Algorithm (IMOALA), incorporating three novel components: an elite selection strategy, a differential-guided external archiving strategy, and a non-uniform mutation strategy. The performance of the IMOALA is benchmarked against other algorithms across twelve test functions and further validated on IEEE 30-bus and 39-bus systems to solve environmental economic dispatch that balances minimal fuel cost and pollutant emission. Results across Inverted Generational Distance (IGD), Maximum Spread (MS), and Generational Distance (GD) metrics demonstrate that the IMOALA achieves superior convergence precision and solution diversity in numerical tests. In engineering applications focusing on fuel cost and emission reduction, the IMOALA consistently yielded higher Normalized Distance (ND) and lower Spacing (SP) values compared to mainstream competitors, delivering evenly distributed Pareto trade-off solutions for cost–emission coordination. These findings verify the feasibility, robustness, and superiority of the IMOALA, offering a highly effective optimization tool for complex, multi-objective power system dispatch and broader engineering challenges.

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.

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

With the rise of the concept of green manufacturing, incorporating energy consumption-related objectives into scheduling problems has become an important research field. Combined with actual production scenarios, this study constructs a mathematical model for the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions. To address the limitation of the traditional Genetic Algorithm (GA) in terms of insufficient local search capability, an Adaptive Genetic Algorithm (AGA) is designed to solve the model. A population initialization method that integrates global and local load minimization is proposed to accelerate the elimination of inferior individuals; the elite retention and roulette wheel selection strategies are combined to prevent the algorithm from falling into local optima. Simulation tests based on standard benchmark instances show that the improved GA can effectively solve the MO-FJGSP, significantly improving both the solution speed and quality. This study provides a novel methodological approach to optimizing production scheduling in green manufacturing environments.

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

Task scheduling in multi-core systems is a critical NP-hard optimization problem that significantly impacts system performance and resource utilization. This paper proposes a novel hybrid approach combining Bee Colony Optimization (BCO) and Genetic Algorithm (GA) for efficient task scheduling in multi-core processor systems. The hybrid BCO-GA algorithm leverages the global exploration capabilities of BCO and the exploitation strengths of GA to achieve optimal task-to-core assignments while minimizing makespan and balancing system load. The proposed approach incorporates adaptive parameter tuning, elite preservation strategies, and dynamic population management to enhance convergence speed and solution quality. Experimental evaluation using standard benchmark task graphs demonstrates that the hybrid BCO-GA algorithm achieves an average makespan reduction of 18.7% compared to standalone BCO, 15.3% compared to pure GA, and 23.4% compared to the Heterogeneous Earliest Finish Time (HEFT) algorithm. The results also show improved load balancing with a 21.5% reduction in load imbalance factor and 16.8% enhancement in processor utilization. The proposed hybrid approach demonstrates superior performance in handling various task graph characteristics including different Communication-to-Computation Ratios (CCR), task counts, and dependency structures, making it a robust solution for multi-core task scheduling problems.

Solving Flow-Shop Scheduling Problems with Random Machine Breakdown and Limited Buffer Using a Pigeon-Inspired Hybrid Artificial Bee Colony Algorithm

A hybrid algorithm combining two metaheuristics is proposed to solve the flow-shop scheduling problem, aiming to minimise the makespan (Cmax). This approach accounts for random machine failures and limited buffer capacity between machines. Since flow-shop scheduling problems are NP-hard, the metaheuristics could be used to solve them effectively. Researchers proved that the hybridisation of metaheuristics would improve the solution quality. Therefore, this study hybridises the recently developed Pigeon-Inspired Optimisation Algorithm (PIOA) with the artificial bee colony (ABC) algorithm. The initial solutions are generated using a dynamic generation technique that relies on a set of constructive heuristics. The optimal solutions from the PIOA serve as input for the ABC algorithm. Various local search and variable neighbourhood search methods are also included to enhance solution quality. Extensive computational experiments, which focus on industrial scheduling scenarios and benchmark problem instances, are conducted to test the performance of the hybrid algorithm. Statistical analysis shows that the proposed algorithm outperforms other algorithms found in the existing literature.

Multi-objective narwhal optimizer: a novel algorithm for multi-criterion optimization