This paper introduces a new metaheuristic, Single Seekers Society (SSS) algorithm, for solving unconstrained and constrained continuous optimization problems. The proposed algorithm aims to simulate the behaviour of a group of people living together, both individually and holistically. The SSS algorithm brings together several single-solution based search algorithms, single seekers, while realizing an information sharing mechanism based on the superposition principle and the reproduction procedure. Each single seeker tries to improve one single solution by using randomly generated parameter set until the stopping condition is reached. Then, the SSS algorithm exchanges partial information between the best solutions identified by the single seekers via the reproduction process. This characteristic generates new solutions to set as the starting point of the single seekers for their next run and provides a satisfactory level of diversification for the SSS algorithm. Additionally, the SSS algorithm determines a target point via the superposition principle at each iteration to make the single seekers to direct their discovery towards this target point. Thus, the SSS algorithm has the feature providing to share the information produced by the single seekers through the reproduction and the superposition principle. The performance of the proposed SSS algorithm is tested on the well-known unconstrained and constrained continuous optimization problems through a set of computational studies. This paper compares SSS algorithm against 27 and 17 different search algorithms on unconstrained and constrained problems, respectively. The experimental results indicate the stability and the effectiveness of the SSS algorithm in terms of quality of produced results, achieved level of convergence and the capability of coping with trapping in local optima and stagnation problems. (C) 2018 Elsevier B.V. All rights reserved.