A novel swarm intelligence optimization technique is proposed called dragonfly algorithm (DA). The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours of dragonflies in nature. Two essential phases of optimization, exploration and exploitation, are designed by modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically. The paper also considers the proposal of binary and multi-objective versions of DA called binary DA (BDA) and multi-objective DA (MODA), respectively. The proposed algorithms are benchmarked by several mathematical test functions and one real case study qualitatively and quantitatively. The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature. The results of MODA also show that this algorithm tends to find very accurate approximations of Pareto optimal solutions with high uniform distribution for multi-objective problems. The set of designs obtained for the submarine propeller design problem demonstrate the merits of MODA in solving challenging real problems with unknown true Pareto optimal front as well. Note that the source codes of the DA, BDA, and MODA algorithms are publicly available at http://www.alimirjalili.com/DA.html.

Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems

A survey on new generation metaheuristic algorithms

This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field Optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, a possible solution is an electromagnetic particle made of electromagnets, and the number of electromagnets is determined by the number of variables of the optimization problem. EFO is a population-based algorithm in which the population is divided into three fields (positive, negative, and neutral); attraction–repulsion forces among electromagnets of these three fields lead particles toward global minima. The golden ratio determines the ratio between attraction and repulsion forces to help particles converge quickly and effectively. The experimental results on 30 high dimensional CEC 2014 benchmarks reflect the superiority of EFO in terms of accuracy and convergence speed over other state-of-the-art optimization algorithms.

Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm

Utilizing the numerical analysis and optimization method for extracting solar cells model parameters, one recurrent issue refers to the difficulty in initializing the parameters. Moreover, those methods using solar cells exponential model are sensible to small changes in the data measured. A chaotic particle swarm optimization algorithm (CPSO) was presented for extracting solar cell model parameters, in which the global search performance and local convergence of particle swarm optimization (PSO) were improved by introducing a chaos search. The CPSO searched for optimal parameters without strict limitation on the search ranges. The procedure is illustrated by applying it to parameters extraction using the current-voltage data measured from a silicon cell and a solar module. The results demonstrate that the method can reduce the influence of experimental data measurement accuracy, and the statistical analysis data of fitting (I–V) characteristics curves are better than that of other published methods.

Extracting solar cell model parameters based on chaos particle swarm algorithm

Transfer functions are considered the simplest and cheapest operators in designing discrete heuristic algorithms. The main advantage of such operators is the maintenance of the structure and other continuous operators of a continuous algorithm. However, a transfer function may show different behaviour in various heuristic algorithms. This paper investigates the behaviour and importance of transfer functions in improving performance of heuristic algorithms. As case studies, two algorithms with different mechanisms of optimisation were chosen: Gravitational Search Algorithm and Particle Swarm Optimisation. Eight transfer functions were integrated in these two algorithms and compared on a set of test functions. The results show that transfer functions may show diverse behaviours and have different impacts on the performance of algorithms, which should be considered when designing a discrete algorithm. The results also demonstrate the significant role of the transfer function in terms of improved exploration and exploitation of a heuristic algorithm.

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How important is a transfer function in discrete heuristic algorithms

In order to promote the applications of particle swarm optimization algorithm(PSO),and provides the relevant information for the further research on this algorithm,a review on the recent progress of PSO is given.Based on the introduces of PSO's basic principles and mechanism,a thorough investigation on the research progress of PSO is given in aspects of parameter setting,convergence characteristic,topology,hybrid algorithm,and the applications in continuous and discrete domains.Finally,the future research issues of the PSO are given.

Huang Shao-rong

Papers

Survey of particle swarm optimization algorithm