Hybrid biogeographybased optimization with enhanced mutation and CMAES for global optimization problem In recent years, scheduling problems have attracted enormous attentions from practitioners and researches in manufacturing systems, for instance, the scheduling of computing resource in cloud infrastructure and cloud services. The scheduling problems in cloud services, big data and other serviceoriented computing problems are regarded as nonseparable problems. In this paper, a hybrid biogeographybased optimization with the enhanced mutation operator and CMAES (HBBOCMA) is proposed to enhance the ability of exploitation on nonseparable problems and alleviate the rotational variance. In the migration operator, the rotationally invariant migration operator is designed to reduce the dependence of BBO on the coordinate system and control the diversity of population. In the mutation operator, an enhanced mutation operator, which is sampled from the mean value and stand deviation of the variables of population, is employed to effectively escape the local optimum. Furthermore, the CMAES, which has outstanding performance on the nonseparable problem, is applied to extend the exploitation of HBBOCMA. Experimental results on CEC2017 demonstrated the effectiveness of the proposed HBBOCMA.

Parameter Setting of CMAES: A Numerical Study on CEC2019 100Digit Challenge The evolution strategy with covariance matrix adaptation (CMAES) is a wellknown algorithm in the family of evolution strategies. It consists of three main mechanisms: derandomized adaptation, cumulative step size, and covariance matrix adaptation. In this paper we aim to improve the CMA ES and its performance by proper parameter values, better repair mechanism, removal of rankone update, and a spread based step size adaptation mechanism. We tested the proposed algorithm by ten test functions in the CEC2019 100Digit Challenge. The results showed that our algorithm can achieve similar solution quality compared with two recent hybrid adaptive evolutionary algorithms and needs fewer fitness function evaluations.