With the advancement of the manufacturing industry, the investigation of the shop floor scheduling problem has gained increasing importance. The Job shop Scheduling Problem (JSP), as a fundamental scheduling problem, holds considerable theoretical research value. However, finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP. A co-operative-guided ant colony optimization algorithm with knowledge learning (namely KLCACO) is proposed to address this difficulty. This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge. A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO. The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions. A pheromone guidance mechanism, which is based on a collaborative machine strategy, is used to simplify information learning and the problem space by collaborating with different machine processing orders. Additionally, the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution, expanding the search space of the algorithm and accelerating its convergence. The KLCACO algorithm is compared with other high-performance intelligent optimization algorithms on four public benchmark datasets, comprising 48 benchmark test cases in total. The effectiveness of the proposed algorithm in addressing JSPs is validated, demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm’s searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems.

The Differential Evolution (DE) algorithm, which is an efficient optimization algorithm, has been used to solve various optimization problems. In this paper, adaptive dimensional learning with a tolerance framework for DE is proposed. The population is divided into an elite subpopulation, an ordinary subpopulation, and an inferior subpopulation according to the fitness values. The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population, respectively. The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy. If the global optimum is not improved in a specified number of iterations, a tolerance mechanism is applied. Under the tolerance mechanism, the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy, respectively. In addition, the individual status and algorithm status are used to adaptively adjust the control parameters. To evaluate the performance of the proposed algorithm, six state-of-the-art DE algorithm variants are compared on the benchmark functions. The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.