Introduction

Swarm intelligence (SI) is evaluated as an adaptive strategy which takes collective intelligence as a behaviour without centralized control structure on how an individual should behave. The rules of SI are simple, self-organizing, co-evolution and being widely applied in the domains of optimizing, searching methods, research in DNA computing improvement, heating system planning etc. SI paradigm includes bird flocking, cuckoo search, animal herding and fish schooling etc. However, the two dominant subfields of SI are ant colony optimization, inspired by pheromone-trail of the ant behaviour and particle swarm optimization, inspired by flocking and swarming behaviour.

Algorithms

Artificial intelligence (AI) has been viewed as a regulation in computer science. It has been developing and examining frameworks which work logically. Bio-inspired computation, metaheuristics and computational intelligence are the common examples of algorithms from numerous parts of AI. Bio-inspired computation utilizes the computing power to demonstrate the living marvels. Computational intelligence which emphasizes on strategy and outcome can be broadly divided into five dominant fields: swarm intelligence, evolutionary computation, artificial neural networks, artificial immune system and fuzzy systems. This chapter will be focusing on a few swarm intelligence-based algorithms which are inspired by their natural processes.

Types of algorithms

5. Flower pollination algorithm
5. African buffalo optimization algorithm
5. Spider monkey optimization algorithm
5. Social spider algorithm
5. Chicken swarm  optimization algorithm
5.  Lion optimization algorithm
5. Firefly algorithm bat algorithms
5. Bat algorithm

Flower pollination algorithm

When pollen grains are produced, pollinators will spread it among the flowers to either local or global flow of pollination. The process of passing the pollen grains from the stamens to the ovule-bearing organs during pollination is used to model flower pollination algorithm

African buffalo optimization algorithm

This algorithm utilizes only learning parameters, therefore it is a simple and yet easy to implement algorithm which guarantees quick convergence. The efficiency and powerful features of this algorithm are capable in solving knapsack problems. ABO has been validated using travelling salesman benchmark problems to study its cost effectiveness.

Spider monkey optimization algorithm

Spider monkey (SM) [45] is a population-based optimization algorithm which was inspired by the intelligent ways of spider monkeys to search for the most suitable food sources. The excellence of food source corresponds to the fitness of a solution. Major characteristics and the strategies of SM algorithm are similar to artificial bee colony algorithm.

Social spider algorithm

This algorithm can solve a wide range of continuous optimization problems including minimization of molecular potential energy function. SSA has been validated using standard benchmark problems to study its performance. An analysis has been carried out on the performance SSA against particle swarm optimization and artificial bee colony.

Chicken swarm optimization algorithm

CSO has been applied in solving the design of speed reducer efficiently. In that research, a gearbox has been created with the design of most efficient speed. The research on CSO has been promising. It has been used to improve the performance of the greedy algorithm

Lion optimization algorithm

Lion optimization (LO) [30] is a population-based algorithm which was inspired by lion’s social system and collaboration characteristics which can be described with the term ‘pride’. The uniqueness of lion’s social behaviour makes them the strongest mammal in the world. LO is modeled based on two unique behaviours of lion: territorial defense and territorial takeover.

Firefly algorithm

The current population of firefly species is over 2000. The short and rhythmic blazing light of fireflies is an astounding sight in the sky of the tropical and calm areas. This nature capability of fireflies inspired the firefly algorithm (FA)

Bat algorithm

Bat algorithm (BA) ] helps in simplicity and flexibility. It is found to be very efficient in handling nonlinear and multi objective issues. Bats have a special high-level capability of bio-sonar (echolocation) which is used to find their prey, obstacles, roosting crevices detection and discriminate different types of insects.

Conclusion

These techniques were inspired by the natural processes of plants, foraging behaviours of insects and social behaviors of animals. These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems.

The journal invites different types of articles including original research article, review articles, short note communications, case reports, Editorials, letters to the Editors and expert opinions & commentaries from different regions for publication.

A standard editorial manager system is utilized for manuscript submission, review, editorial processing and tracking which can be securely accessed by the authors, reviewers and editors for monitoring and tracking the article processing. Manuscripts can be uploaded online at Editorial Tracking System (https://www.longdom.org/editorial-tracking/publisher.php) or forwarded to the Editorial Office at https://www.longdom.org/swarm-intelligence-evolutionary-computation.html The Journals includes around 150Abstracts and 100 Keynote speakers have given their valuable words. The meet has provided a great scope for interaction of professionals including in addition to clinical experts and top-level pathologists and scientists from around the globe, on a single platform.

Media Contact:

Sarah Rose

Journal Manager

International journal of swarm intelligence and evolutionary computation

Email: evolcomput@journalres.org