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In real life, mostly problems are dynamic. Many algorithms have been proposedto handle the static problems, but these algorithms do not handle or poorlyhandle the dynamic environment problems. Although, many algorithms have beenproposed to handle dynamic problems but still, there are some limitations ordrawbacks in every algorithm regarding diversity of particles and tracking ofalready found optima. To overcome these limitations/drawbacks, we have proposeda new efficient algorithm to handle the dynamic environment effectively bytracking and locating multiple optima and by improving the diversity andconvergence speed of algorithm. In this algorithm, a new method has beenproposed which explore the undiscovered areas of search space to increase thediversity of algorithm. This algorithm also uses a method to effectively handlethe overlapped and overcrowded particles. Branke has proposed moving peakbenchmark which is commonly used MBP in literature. We also have performeddifferent experiments on Moving Peak Benchmark. After comparing theexperimental results with different state of art algorithms, it was seen thatour algorithm performed more efficiently.

A Diverse Clustering Particle Swarm Optimizer for Dynamic Environment: To Locate and Track Multiple Optima