Abstract: This paper presents a novel swarm evolutionary metaheuristic optimization algorithm inspired by the cyclic nature of human civilization in its rise from nomadic life to the point where it peaks, stagnates, and then declines back to nomadic life. Nomads do the exploration, while civics do the exploitation. The proposed algorithm also mimics basic attributes and behaviors of human civilizations from hostility that pushes civilizations away from each other to cooperation and trade which helps to explore areas in between and exchange knowledge of best traits (products). Experimental results show highly competitive results when benchmarked with common swarm-based evolutionary algorithms such as Genetic Algorithm, Particle Swarm Optimization, Wolf Pack Algorithm, and Artificial Bee Colony, using common test functions like Rastrigin, Schwefel, Rosenbrock, Griewank, and Ackley.

Keywords: Metaheuristic Optimization, Swarm Intelligence, Swarm-based Optimization, Evolutionary Algorithms, Evolutionary Optimization, Numerical Function Optimization

Pages: 286-296