Memetic clonal selection algorithm with EDA vaccination for unconstrained binary quadratic programming problems - Cai, Yiqiao; Wang, Jiahai; Yin, Jian; Zhou, Yalan (Expert systems with applications, 2011)
► MCSA-EDA proposes an estimation of distribution algorithm (EDA) vaccination, as hypermutation and recombination operator, to effectively use the global statistical information to guide the search. ► MCSA-EDA introduces three components, i.e., EDA vaccination, fitness uniform selection scheme and adaptive Tabu search, to overcome the deficiencies of the conventional clonal selection algorithm. ► MCSA-EDA has been shown as a competitive approach for unconstrained binary quadratic programming problems (UBQP) and thus as a powerful and effective framework for combinatorial optimization. This paper presents a memetic clonal selection algorithm (MCSA) with estimation of distribution algorithm (EDA) vaccination, named MCSA-EDA, for the unconstrained binary quadratic programming problem (UBQP). In order to improve the performance of the conventional clonal selection algorithm (CSA), three components are adopted in MCSA-EDA. First, to compensate for the absence of recombination among different antibodies, an EDA vaccination is designed and incorporated into CSA. Second, to keep the diversity of the population, a fitness uniform selection scheme (FUSS) is adopted as a selection operator. Third, to enhance the exploitation ability of CSA, an adaptive tabu search (TS) with feedback mechanism is introduced. Thus, MCSA-EDA can overcome the deficiencies of CSA and further search better solutions. MCSA-EDA is tested on a series of UBQP with size up to 7000 variables. Simulation results show that MCSA-EDA is effective for improving the performance of the conventional CSA and is better than or at least competitive with other existing metaheuristic algorithms.