Swarm Intelligence Techniques and Their Adaptive Nature with Applications

Swarm based techniques have huge application domain covering multiple disciplines, which include power system, fuzzy system, forecasting, bio-medicine, sociological analysis, image processing, sound processing, signal processing, data analysis, process modeling, process controlling etc. In last two decades numerous techniques and their variations have been developed. Despite many variations are being carried out, main skeleton of these techniques remain same. With diverse application domains, most of these techniques have been modified to fit into a particular application. These changes undergo mostly in perspective of encoding scheme, parameter tuning and search strategy. Sources of real world problems are different, but their nature sometimes found similar to other problems. Hence, swarm based techniques utilized for one of these problems can be applied to others as well. As sources of these problems are different, applicability of such techniques are very much dependent on the problem. Same encoding scheme may not be suitable for the other similar kind of problems, which has led to development of problem specific encoding schemes. Sometimes found that, even though encoding scheme is compatible to a problem, parameters used in the technique does not utilized in favor of the problem. So, parameter tuning approaches are incorporated into the swarm based techniques. Similarly, search strategy utilized in swarm based techniques are also vary with the application domain. In this chapter we will study those problem specific adaptive nature of swarm based techniques. Essence of this study is to find pros and cons of such adaptation. Our study also aims to draw some aspects of such problem specific variations through which it can be predicted that what kind of adaptation is more convenient for any real world problem.