Minimization algorithms based on supervisor and searcher cooperation

In the present work, we explore a general framework for the design of new m inimization algorithms with desirable characteristics, namely, supervisor-s earcher cooperation. We propose a class of algorithms within this framework and examine a gradient algorithm in the class. Global convergence is estab lished for the deterministic case in the absence of noise and the convergen ce rate is studied. Both theoretical analysis and numerical tests show that -the algorithm is efficient for the deterministic case. Furthermore, the fa ct that there is no line search procedure incorporated in the algorithm see ms to strengthen its robustness so that it tackles effectively test problem s with stronger stochastic noises. The numerical results for both determini stic and stochastic test problems illustrate the appealing attributes of th e algorithm.