The desirable merits of the intelligent computational algorithms and the initial success in many domains have encouraged researchers to work towards the advancement of these techniques. A major plunge in algorithmic development to solve the increasingly complex problems turned out as breakthrough towards the development of computational intelligence (CI) techniques. Nature proved to be one of the greatest sources of inspiration for these intelligent algorithms. In this chapter, computational intelligence techniques inspired by insects are discussed. These techniques make use of the skills of intelligent agent by mimicking insect behavior suitable for the required problem. The diversities in the behavior of the insect families and similarities among them that are used by researchers for generating intelligent techniques are also discussed in this chapter.
TopIntroduction
The term “computational intelligence” was coined by Bezdek (1994) based on the intelligent behavior of computer motivated by the nature. Computational Intelligence (CI) (Bezdel, 1994) is the study of the design of intelligent agents based system. The agents can be a brainwave from insects, worms, animals, airplane, human, organizations, society and many more. It utilizes the skills of intelligent agents for required results as illustrated in Figure 1. CI had opened numerous brand new dimensions of the scientific research in past two decades.
Figure1. Computational intelligence agents and the driving skills
Computational intelligence can be grouped into several families depending on its functionality and the source of inspiration. It can be inspired by physics (Karaboga & Akay, 2009) like Central Force Optimization (CFO), Big Bang-Big Crunch (BBC), and Particle Collision Algorithm (PCA). There are several techniques inspired by chemistry (Karaboga & Akay, 2009) like Artificial Chemical Process (ACP), Chemical Reaction Algorithm (CRA), and Gases Brownian Motion Optimization (GBMO). Techniques inspired by mathematics are Base Optimization Algorithm (BOA) and Matheuristics.
There are several biological inspirations for innovative CI. These can be further grouped into multiple classes like animals, birds, tribes, plants, etc. depending upon the nature of the problem (Karaboga & Akay, 2009). There are many algorithms bagged into each category like techniques motivated from animal includes Cat Swarm Optimization (CSO), Monkey Search, Wolf Pack Search (WPS). Artificial Fish Swarm Algorithm (AFSA), Fish School Search (FSS), Shark-Search Algorithm (SSA) and many more techniques are based on aquatic animals. Techniques like Cuckoo Optimization Algorithm (COA), Dove Swarm Optimization (DSO), and Migrating Birds Optimization (MBO) are inspired by birds. Bacterial Foraging Algorithm (BFA), Amoeboid Organism Algorithm (AOA) and several others are inspired by microorganisms. Frog Calling Algorithm (FCA). Shuffled Frog Leaping Algorithm (SFLA) is inspired by amphibians. Plants inspired algorithms include Paddy Field Algorithm (PFA), Invasive Weed Optimization (IWO). Saplings Growing Up Algorithm (SGUA).
Over the past two decades from the evidence of the promising results of many researches, technologies inspired by insects have enjoyed widely acceptance and an extraordinary attractiveness that had opened brand new aspects for scientific research. The primary focus of this chapter is on families of insects contributing towards the development of Computational Intelligence. Figure 2 shows the family tree of the computational intelligence leading to insects.
Figure 2. Family of computational intelligence