In practical applications, particularly in flexible manufacturing systems, there is a high level of uncertainty. A type-2 fuzzy logic system (T2FS) has several parameters and an enhanced ability to handle high levels of uncertainty. This study proposes an improved artificial immune system (IAIS) algorithm to solve a special case of the flexible job shop scheduling problem (FJSP), where the processing time of each job is a nonsymmetric triangular interval T2FS value. First, a novel affinity calculation method considering the T2FS values is developed. Then, four problem-specific initialization heuristics and six local search approaches considering the constraints and objectives are proposed. To enhance the exploration abilities of the algorithm, a simulated annealing method is embedded to accept antibodies with low affinity. In addition, a novel population diversity heuristic is developed to eliminate antibodies with high crowding values. Six efficient algorithms are selected for a detailed comparison, and the simulation results demonstrate that the proposed IAIS algorithm is effective for T2FS FJSPs.

In this study, an improved invasive weed optimisation (CMIWO) algorithm is investigated to solve the dynamic economic dispatch (DED) problem with valve-point effects. In the proposed algorithm, a hybrid operator including selective crossover, random mutation and row crossover is proposed to improve the exploration and exploitation abilities. Moreover, a self-adaption repair method is developed and embedded into the proposed algorithm to repair infeasible solutions. To verify the optimisation performance of CMIWO, six well-known DED problems in three different-scale power systems are tested and compared with other algorithms that have been proposed in the literature. The experimental results show that CMIWO can find the more economical dispatch solutions compared to other algorithms, and the self-adaption repair method can successfully convert infeasible solutions into feasible solutions. The convergence ability of CMIWO is also verified after the detailed comparison.

In this study, a hybrid invasive weed optimization (HIWO) algorithm that hybridizes the invasive weed optimization (IWO) algorithm and genetic algorithm (GA) has been proposed to solve economic dispatch (ED) problems in power systems. In the proposed algorithm, the IWO algorithm is used as the main optimizer to explore the solution space, whereas the crossover and mutation operations of the GA are developed to significantly improve the optimization ability of IWO. In addition, an effective repair method is embedded in the proposed algorithm to repair infeasible solutions by handing various practical constraints of ED problems. To verify the optimization performance of the proposed algorithm and the effectiveness of the repair method, six ED problems in the different-scale power systems were tested and compared with other algorithms proposed in the literature. The experimental results indicated that the proposed HIWO algorithm can obtain the more economical dispatch solutions, and the proposed repair method can effectively repair each infeasible dispatch solution to a feasible solution. The convergence capability, applicability and effectiveness of HIWO were also demonstrated through the comprehensive comparison results.