A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection

To overcome the shortcoming of poor diversity of Artificial Bee Colony(ABC)algorithm,this paper presents a modified Artificial Bee Colony algorithm.Unlike ABC in which onlooker bees choose employed bees from the colony according to proportional fitness choosing strategy,in MABC,in order to decrease the selection pressure and further improve the diversity,it does not set onlooker bees,as a result of no selection pressure on employed bees(solutions).The simulation results show that the presented algorithm can ensure the colony diversity and improve the performances of ABC.

A Modified Artificial Bee Colony Algorithm

Improving artificial Bee colony algorithm using a new neighborhood selection mechanism

A novel parameter-free meta-heuristic optimization algorithm known as the golden ratio optimization method (GROM) is proposed. The proposed algorithm is inspired by the golden ratio of plant and animal growth which is formulated by the well-known mathematician Fibonacci. He introduced a series of numbers in which a number (except the first two numbers) is equal to the sum of the two previous numbers. In this series, the ratio of two consecutive numbers is almost the same for all the numbers and is known as golden ratio. This ratio can be extensively found in nature such as snail lacquer part and foliage growth of trees. The proposed approach employed this golden ratio to update the solutions in an optimization algorithm. In the proposed method, the solutions are updated in two different phases to achieve the global best answer. There is no need for any parameter tuning, and the implementation of the proposed method is very simple. In order to evaluate the proposed method, 29 well-known benchmark test functions and also 5 classical engineering optimization problems including 4 mechanical engineering problems and 1 electrical engineering problem are employed. Using several test functions, the performance of the proposed method in solving different problems including discrete, continuous, high dimension, and high constraints problems is testified. The results of the proposed method are compared with those of 11 well-regarded state-of-the-art optimization algorithms. The comparisons are made from different aspects such as the final obtained answer, the speed and behavior of convergence, and CPU time consumption. Superiority of the purposed method from different points of views can be concluded by means of comparisons.

A novel meta-heuristic optimization method based on golden ratio in nature

Theoretical analysis of swarm intelligence and evolutionary algorithms is relatively less explored area of research. Stability and convergence analysis of swarm intelligence and evolutionary algorithms can help the researchers to fine tune the parameter values. This paper presents the stability analysis of a famous Artificial Bee Colony (ABC) optimization algorithm using von Neumann stability criterion for two-level finite difference scheme. Parameter selection for the ABC algorithm is recommended based on the obtained stability conditions. The findings are also validated through numerical experiments on test problems.

Stability analysis of Artificial Bee Colony optimization algorithm

The topological design of a computer communication network is a well-known NP-hard problem. The problem complexity is further magnified by the presence of multiple design objectives and numerous design constraints. This paper presents a goal programming-based multi-objective artificial bee colony optimization (MOABC) algorithm to solve the problem of topological design of distributed local area networks (DLANs). Five design objectives are considered herein, namely, network reliability, network availability, average link utilization, monetary cost, and network delay. Goal programming (GP) is incorporated to aggregate the multiple design objectives into a single objective function. A modified version of MOABC, named as evolutionary multi-objective ABC (EMOABC) is also proposed which incorporates the characteristics of simulated evolution (SE) algorithm for improved local search. The effect of control parameters of MOABC is investigated. Comparison of EMOABC with MOABC and the standard ABC (SABC) shows better performance of EMOABC. Furthermore, a comparative analysis is also done with non-dominated sorting genetic algorithm II (NSGA-II), Pareto-dominance particle swarm optimization (PDPSO) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms, namely, MOEA/D-1 and MOEA/D-2. Results indicate that EMOABC demonstrated superior performance than all the other algorithms.

Amani Saad

Papers

A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design