Swarm intelligence describes the ability of groups of social animals and insects to exhibit highly organized and complex problem-solving behaviors that allow the group as a whole to accomplish tasks which are beyond the capabilities of any one of the constituent individuals. This natural phenomenon is the inspiration for swarm intelligence systems, a class of algorithms that utilizes the emerge...

Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarm...

Global optimization methods play an important role to solve many real-world problems. Flower pollination algorithm (FP) is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new hybrid optimization method called hybrid flower pollination algorithm (FPPSO) is proposed. The method combines the standard flower pollination algorithm (FP) with the par...

Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work inves...

Recently, the scheduling problem in distributed data-intensive computing environments has been an active research topic. This Chapter models the scheduling problem for work-flow applications in distributed dataintensive computing environments (FDSP) and makes an attempt to formulate the problem. Several meta-heuristics inspired from particle swarm optimization algorithm are proposed to formulat...

Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, some of them, such as Particle Swarm Optimization, may not present the ability to generate diversity after environmental changes. In this paper we propose a hybrid algorithm to overcome this problem by applying a very interesting feature of the Fish School Search algorithm to the Particle Swarm ...

This paper presents a leader glowworm swarm optimization algorithm (LGSO) for solving nonlinear equations systems. Since glowworm swarm optimization algorithm has bad optimized ability at high dimension, proposing glowworm swarm optimization algorithm with leader mechanism to strengthen the global optimization ability. Through various types nonlinear equations testing, experiment results show t...

Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. This paper presents Bee Swarm Optimization intended to introduce chaotic sequences and psychology factor of emotion into the algorithm. We define two emotions Bees could have, positive and negative, and correspond to two reaction to perception respectively. For avoiding premature c...

Evolutionary Computation (EC) is a research area of metaheuristics mainly applied to real-world optimization problems. EC is inspired by biological mechanisms such as reproduction, mutation, recombination, natural selection and collective animal behavior. Two branches of EC can be highlighted: Evolutionary Algorithms (EA) comprising Genetic Algorithms (Goldberg, 1989), Genetic Programming (Koza...

This paper presents an algorithm called augmented Lagrangian particle swarm optimization with velocity limits (VL-ALPSO). It uses a particle swarm optimization (PSO) based algorithm to optimize the motion planning for swarm mobile robots. Considering problems with engineering constraints and obstacles in the environment, the algorithm combines the method of augmented Lagrangian multipliers and ...