A new optimization algorithm for solving NP-hard problems

The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search that belong to the Evolutionary Computations Algorithms (ECs) are not suitable for fine tuning structures as they neglect all conventional heuristics. In most of the NP-hard problems, the best solution rarely be completely random, it follows one or more rules (heuristics). In this paper a new algorithm titled “Shaking Optimization Algorithm” is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The proposed approach is applied to the Job Shop Scheduling problems (JSS) and gives promising results compared with that of GA, PSO, SA, and TS algorithms.