# Fuzzy Fireworks Algorithm Based on a Sparks Dispersion Measure
## 2 Literature Review  
Swarm intelligence is the study of computational systems inspired by 'collective intelligence'. Collective intelligence emerges through the cooperation of large numbers of homogeneous agents in the environment. Examples include schools of fish, flocks of birds, and colonies of ants. Such intelligence is decentralized, self-organizing, and distributed throughout an environment. In nature, such systems are commonly used to solve problems such as effective foraging for food, prey evasion, or colony re-location. The information is typically stored throughout the participating homogeneous agents, or is stored or communicated in the environment itself such as through the use of pheromones in ants, dancing in bees, and proximity in fish and birds [2]. In recent years, swarm intelligence (SI), has been very popular among researchers working on optimization problems around the world [3]. Some examples of SI are the following algorithms [4]:  
* PSO (Particle Swarm Optimization)
* ACO (Ant Colony Optimization)
* ABC (Artificial Bee Colony)
* GA (Genetic Algorithm)  
Particle Swarm Optimization (PSO) is inspired by the social foraging behavior of some animals such as flocking behavior of birds and the schooling behavior of fish. The goal of the algorithm is to have all the particles locate the optima in a multi-dimensional hyper-volume. This is achieved by assigning initially random positions to all particles in the space and small initial random velocities. The algorithm is executed like a simulation, advancing the position of each particle in turn based on its velocity, the best known global position in the problem space, and the best position known to a particle. The objective function is sampled after each position update. Over time, through a combination of exploration and exploitation of known good positions in the search space, the particles cluster or converge together around optima, or several optima [5]. The Ant Colony System (ACO) algorithm is inspired by the foraging behavior of ants, specifically the pheromone communication between ants regarding a good path between the colony and a food source in an environment. Ants initially wander randomly around their environment. Once food is located, an ant will begin laying down pheromone in the environment. Numerous trips between the food and the colony are performed, and if the same route is followed that leads to food, then additional pheromone is laid down. Pheromone decays in the environment, so that older paths are less likely to be followed. Other ants may discover the same path to the food and in turn may follow it and also lay down pheromone. A positive feedback process routes more and more ants to productive paths that are in turn further refined through use [6]. The Bees Algorithm (ABC) is inspired by the foraging behavior of honey bees. Honey bees collect nectar from vast areas around their hive (more than 10 km). Bee Colonies have been observed to send bees to collect nectar from flower patches relative to the amount of food available at each patch. Bees communicate with each other at the hive via a waggle dance that informs other bees in the hive as to the direction, distance, and quality rating of food sources. The strategy of this algorithm is the information processing to achieve the objective, which is to locate and explore good sites within a problem search space. Scouts are sent out to randomly sample the problem space and locate good sites. The good sites are exploited via the application of a local search, where a small number of good sites are explored more than the others. Good sites are continually exploited, although many scouts are sent out in each iteration in search of additional good sites [7]. The Genetic Algorithm (GA) is inspired by population genetics (including heredity and gene frequencies) and evolution at the population level, as well as the Mendelian understanding of the structure (such as chromosomes, genes, alleles) and mechanisms (such as recombination and mutation).