Swarm intelligence algorithms are designed to mimic the natural behaviors of living organisms. The birds, animals and insects exhibit extraordinary problem solving behaviors and intelligence when living in colonies or groups. These unique behaviors form the basis for the design of the Metaheuristic which are helpful in solving several real-life combinatorial optimization problems. Monkey algorithm is developed based on the unique behaviors of monkeys such as mountain and tree climbing, jumping, watching and somersaulting. This paper reports for the first time the design and development of Multi-objective Monkey Algorithm (MoMA) and its use for the design of molecules with optimal drug-like properties. Finally, the performance of the proposed MoMA for Drug design (MoMADrug) is compared with the previously disclosed Multi-objective Genetic algorithm (MoGADdrug) for the design of drug-like molecules.

Localization in Vehicular Ad Hoc Networks is a challenging issue due to the dynamic topology and high mobility of vehicles. Finding the exact location of the vehicles is not possible in this network due to the vehicles moving speed. In vehicular ad hoc networks, various kinds of localization techniques are used to know the position and the location of the vehicles. These techniques do have some limitations due to obstacles on the roadside, building shadow, tunnels and bad weather condition, etc. This paper is motivated to propose an algorithm to increase the localization accuracy and localization error. Acknowledgement based Localization Method (ALM) is used to improve the vehicle’s positioning information while broadcasting an Emergency Safety Message. ALM updates the position information whenever the vehicle changes its position. The proposed algorithm is compared with RSSI, TOA and DLM based localization techniques. The result shows that ALM algorithm improves the accuracy level and reduces the error rate caused by incorrect position estimation.

Swarm intelligence algorithms (SIA) are bio-inspired techniques based on the intelligent behavior of various animals, birds, and insects. SIA are problem-independent and are efficient in solving real world complex optimization problems to arrive at the optimal solutions. Monkey behavior based algorithms are one among the SIAs first proposed in 2007. Since then, several variants such as Monkey search, Monkey algorithm, and Spider Monkey optimization algorithms have been proposed. These algorithms are based on the tree or mountain climbing and food searching behavior of monkeys either individually or in groups. They were designed with various representations, covering different behaviors of monkeys and hybridizing with the efficient operators and features of other SIAs and Genetic algorithm. They were explored for applications in several fields including bioinformatics, civil engineering, electrical engineering, networking, data mining etc. In this survey, we provide a comprehensive overview of monkey behavior based algorithms and their related literatures and discuss useful research directions to provide better insights for swarm intelligence researchers.

Most of the man-made technologies are nature-inspired including the popular heuristics or meta-heuristics techniques that have been used to solve complex computational optimization problems. In most of the metaheuristics algorithms, adjusting the parameters has important significance to obtain the best performance of the algorithm. Cricket Chirping Algorithm (CCA) is a nature inspired meta-heuristic algorithm that has been designed by mimicking the chirping behavior of the cricket (insect) for solving optimization problems. CCA employs a set of parameters for its smooth functioning. In a metaheuristic algorithm, controlling the values of various parameters is one of the most important issues of research. While solving the problem, the parameter value control has a potential to improve the efficiency of the algorithm. The different parameters used in CCA are tuned for better performance of the algorithm and experiment its impact on a set of sample benchmark test functions, then the fine-tuned CCA is compared with some other meta-heuristic algorithms. The results show the optimal choice of the various parameters to solve optimization problems using CCA.