Grey Wolf Optimizer

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

Slime mould algorithm: A new method for stochastic optimization

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed for global optimization of complex functions In the proposed FA, two types of explosion (search) processes are employed, and the mechanisms for keeping diversity of sparks are also well designed In order to demonstrate the validation of the FA, a number of experiments were conducted on nine benchmark test functions to compare the FA with two variants of particle swarm optimization (PSO) algorithms, namely Standard PSO and Clonal PSO It turns out from the results that the proposed FA clearly outperforms the two variants of the PSOs in both convergence speed and global solution accuracy.

Fireworks algorithm for optimization

Cellular Robotic Systems are capable of ’intelligent* behavior. The meaning of this intelligence is analyzed in the paper. We define robot intelligence and robot system intelligence in terms of unpredictability of improbable behavior. The concept of unpredictability is analyzed in relation to (1) statistical unpredictability, (2) inaccessibility, (3) undecidability, (4) intractability, and (5) non-representability. We argue that the latter two type of unpredictability, when exhibited by systems capable of producing order, can result in a non-trivial, different form of intelligent behavior (Swarm Intelligence). Engineering problems related to Swarm Intelligence are mentioned in relation to Cellular Robotic Systems which consist of collections of autonomous, non-synchronized, non-intelligent robots cooperating to achieve global tasks.

Jing Wang

Papers

Swarm Intelligence in Cellular Robotic Systems