Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm

Abstract

<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>

Evaluation of the stability enhancement of the conventional sliding mode controller using whale optimization algorithm

“…Numerous conventional methods were offered for addressing this issue; one of them is substituting the Sign mathematical function, which is considered the cause of the chattering phenomenon by another model [16,17]. While some solutions were based on the integration between CSMCR and smart fuzzy controller [18], some of the Optimization techniques also investigated to reduce chattering impact such as genetic and particle swarm algorithms [19][20][21][22][23]. However; the deployment of optimization algorithms requires further investigation to illustrate the feasibility of the advanced techniques over classical mathematical models, the proposed work mainly aims to utilize a highly advanced whale optimization algorithm in order to diminish as much as possible the impact of chattering behavior and thus achieving reliable and consistent stability by finding best values of gain G and the slope of sliding surface δ for (CSMCR) to ensure the stability of single inverted pendulum as a nonlinear system case study.…”

Improved Chicken Swarm Optimization Algorithm to Solve the Travelling Salesman Problem

“…Step 6: Find a new solution by updating the position of each rooster, hens and chicks using the new Equations (9), (10) and (11).…”

“…The difficulty of this problem has generated much interest to solve TSP, starting with the implementation of heuristics methods such as Tabu Search (TS) [5], Genetic Algorithm (GA) [6], Heuristic Approach [7], Greedy Randomize Adaptive Search Procedure (GRASP) [8], and Simulated Annealing (ST) [9]. Recently, several studies use of the bio-inspired algorithms using swarm intelligence methods [10] such as: ant colonies optimization (ACO) [11], particle swarm optimization (PSO) [12], [13] bee colonies optimization (BCO) [14], harmony search algorithm (HS) [15], [16], bat-inspired algorithm (BA) [17], [18],cuckoo search (CS) [19], [20] and a bio-inspired hunting search algorithm (HUS) [21].…”

Key Generation from Multibiometric System Using Meerkat Algorithm

“…The aim behind the use of an MCA method is that a key stream is selected according to the distribution of characters in the plaintext. Which would give the ability to encode characters in the key stream which occur in the plaintext [10].…”