Stability Analysis of Artificial Bee Colony Optimization Algorithm

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

An augmented lagrangian fish swarm based method for global optimization

This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical results with a benchmark set of problems are shown, including a comparison with other stochastic-type algorithms.Fundação para a Ciência e a Tecnologia (FCT

Convergence proof of an enhanced particle swarm optimisation method integrated with evolutionary game theory

This paper proposes an enhanced Particle Swarm Optimisation (PSO) algorithm and examines its performance. In the proposed PSO approach, PSO is combined with Evolutionary Game Theory to improve convergence. One of the main challenges of such stochastic optimisation algorithms is the difficulty in the theoretical analysis of the convergence and performance. Therefore, this paper analytically investigates the convergence and performance of the proposed PSO algorithm. The analysis results show that convergence speed of the proposed PSO is superior to that of the Standard PSO approach. This paper also develops another algorithm combining the proposed PSO with the Standard PSO algorithm to mitigate the potential premature convergence issue in the proposed PSO algorithm. The combined approach consists of two types of particles, one follows Standard PSO and the other follows the proposed PSO. This enables exploitation of both diversification of the particles’ exploration and adaptation of the search direction

Formation Control of Robotic Swarm Using Bounded Artificial Forces

Formation control of multirobot systems has drawn significant attention in the recent years. This paper presents a potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions. The algorithm applies in both stationary and moving targets formation. We define the bounded artificial forces in the form of exponential functions, so that the behavior of the swarm drove by the forces can be adjusted via selecting proper control parameters. The theoretical analysis of the swarm behavior proves the stability and convergence properties of the algorithm. We further make certain modifications upon the forces to improve the robustness of the swarm behavior in the presence of realistic implementation considerations. The considerations include obstacle avoidance, local minima, and deformation of the shape. Finally, detailed simulation results validate the efficiency of the proposed algorithm, and the direction of possible futrue work is discussed in the conclusions

Бионический параметрический паретооптимальный синтез аксиально-симметричных источников магнитных полей с ферромагнитными элементами конструкции

In this research a method of efficient parametric synthesis of axially symmetric magnetic field sources with ferromagnetic elements has been developed that allows finding a solution of optimization problem in multicriterion statement. Problem of magnetic field analysis is solved with taking into account linear and nonlinear properties of ferromagnetic with using of border and volume integral equations approach. Developed by authors a hybrid optimization algorithm by swarm of particles with evolutionary formation of swarm composition is used to provide a seeking of global solution of the problem. This algorithm has high convergence speed, and it is economical in terms of an objective function computations

Efficiency Analysis of Swarm Intelligence and Randomization Techniques

Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark. The outstanding performance and efficiency of swarm-based algorithms inspired many new developments, though mathematical understanding of metaheuristics remains partly a mystery. In contrast to the classic deterministic algorithms, metaheuristics such as PSO always use some form of randomness, and such randomization now employs various techniques. This paper intends to review and analyze some of the convergence and efficiency associated with metaheuristics such as firefly algorithm, random walks, and L\'evy flights. We will discuss how these techniques are used and their implications for further research.Comment: 10 pages. arXiv admin note: substantial text overlap with arXiv:1212.0220, arXiv:1208.0527, arXiv:1003.146