Genetic algorithm (GA) is most famous and useful algorithm belongs to the class of Evolutionary algorithm (EA).However, often an objective function possesses numerous local optima, which could trap GA from moving toward the desired global solution.GA provides extremely long length of binary string, unsure position of two chromosomes crossover, turbid mutation methods, which fearfully limit the efficiency of GA.In this paper, we propose a new metaheuristic optimization algorithm named as λ algorithm.The new algorithm utilizes strings of digits from member set {0, 1, 2, 3, and 4} to represent the fitness values of candidate solutions (represented as vectors in n-dimensional Euclidean space).The λ algorithm draws useful information from both repeated and unrepeated digits of strings (candidate solutions), to simulate global advanced schema towards final optimization.The new algorithm only asks 3 or 4 digits to represent an unknown variable,but still could access very precision results.Disciplinary λ comparison and expansion operations instead of inefficient mutation operation, which allowed the strings select more efficient schema from the digits.Without using crossover, stochastic coding strategy, population selection, PSO methods...all of the existing optimization methods, the new algorithm still could achieve the highest searching efficiency, better optimized than most of existing algorithm.