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Differential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations.

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This paper presents a multi-resolution feature extraction technique to speech recognition. The proposed multi-resolution feature extraction technique uses wavelet transform and wavelet packet to calculate features of each sub-band in order not to spread noise distortions over the entire feature space. In our previous works, we had developed a method for speech classification. For speech classification, the universe of discourse is divided into many types, and each type is treated as a class. The hyper-rectangular fuzzy system is used to classify frames and integrate the rule-based approach. The variances of each sub-band are utilized to extract both crisp and fuzzy classification rules. In our experiments, the Texas Instruments/Massachusetts Institute of Technology database is used and extracts features of phonemes. The results demonstrate the superior performance to Mel frequency cepstral coefficients. The effectiveness of the proposed system is encouraging.