Biblio

In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm’s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.

Metaheuristic search technique is one of the advance approach when compared with traditional heuristic search technique. To select one option among different alternatives is not hard to get but really hard is give assurance that being cost effective. This hard problem is solved by the meta-heuristic search technique with the help of fitness function. Fitness function is a crucial metrics or a measure which helps in deciding which solution is optimal to choose from available set of test sets. This paper discusses hill climbing, simulated annealing, tabu search, genetic algorithm and particle swarm optimization techniques in detail explaining with the help of the algorithm. If metaheuristic search techniques combine some of the security testing methods, it would result in better searching technique as well as secure too. This paper primarily focusses on the metaheuristic search techniques.

The necessity to deploy wireless mesh network is determined by the real world application requirements. WMN does not fit some application well due to latency issues and capacity related problem with paths having more than 2 hops. With the promising IEEE 802.11ac based device a better fairness for multi-hop communications are expected to support broadband application; the rate usually varies according to the link quality and network environment. Careful network planning can effectively improves the throughput and delay of the overall network. We provide model for the placement of router nodes as an optimization process to improve performance. Our aim is to propose a WMNs planning model based on multiobjective constraints like coverage, reliability, and cost of deployment. The bit rate guarantee therefore necessary to limit the number of stations connected to the access point; to takes into account delay and fairness of the network the user's behaviors are derived. We use a multiobjective evolutionary algorithm based metaheuristic to evaluate the performance of our proposed placement algorithm.

The cuttlefish optimization algorithm is a new combinatorial optimization algorithm in the family of metaheuristics, applied in the continuous domain, and which provides mechanisms for local and global research. This paper presents a new adaptation of this algorithm in the discrete case, solving the famous travelling salesman problem, which is one of the discrete combinatorial optimization problems. This new adaptation proposes a reformulation of the equations to generate solutions depending a different algorithm cases. The experimental results of the proposed algorithm on instances of TSPLib library are compared with the other methods, show the efficiency and quality of this adaptation.