swarm algorithms

In recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear, and high dimensions problems have increased dramatically and the fact that meta-heuristic algorithms are used to solve complex and changing problems of real life, has caused the algorithms world and their design to be very dynamic and alive; that's why new algorithms are constantly being created. Hence, the purpose of this research is to introduce a novel meta-heuristic algorithm called Military Optimization Algorithm (MOA). 

Inspired by military operations, the proposed algorithm was designed and presented. After coding, Standard test functions and benchmark algorithms were determined to evaluate the performance of the algorithm.

The performance of new algorithm is analyzed by 23 standard test functions and compared to 8 benchmark meta-heuristic algorithms including: Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, Shuffled Frog Leaping Algorithm, and Imperialist Competitive Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, and Grasshopper Optimization Algorithm, by considering three indices of "average answers", "time complexity of algorithm (speed)" and "Convergence speed/ time".  The results show the excellent performance of the proposed algorithm.

In this paper, inspired by military operations, a novel meta-heuristic algorithm called MOA is introduced. It is population-based and stable with "random search", "dividing solution space into several regions and allocating a part of the population to each region", "cavalry search", and "infantry search".

Swarm robotic (SR) draws inspiration from nature and is emerged as synthesis of swarm intelligence and robotic. Due to special properties like feasibility, robustness and scalability, swarm robotic has several advantages in comparison to other multi robot systems. In this paper, firstly, SR source of inspiration is introduced, and a precise definition along with desirable properties of swarm robotic is presented. Secondly, SR application domains, well-known projects and simulation environments are reviewed. Thirdly, an overview of various modelling approaches and algorithms is reported. The presented algorithms are divided into task-specific categories. So some of common tasks that could be profitably tackled using swarm robotic can be understood. Finally SR open problems and challenges are discussed. We expect this paper can give a comprehensive view of swarm robotic to researchers.