The aim of evolutionary multi/many-objective optimization is to obtain a set of Pareto-optimal
solutions with good trade-off among the multiple conflicting objectives. However, the …

The teaching–learning-based optimization (TLBO) algorithm is a new optimization technique
that has been successfully applied in various optimization fields. However, the TLBO still has …

In conventional optimization problems, it is assumed that all relevant parametric constraints
remain stationary. In contrast, optimization problems encountered in practical applications …

The evolutionary computing (EC) domain provides variety of optimization algorithms to the
computer science community. The increase of large-scale real-world optimization problems …

In the given image identifying the existence of a required object is the concern of the object
detection process. This is quite natural for Human without any effort, however making a …

Control algorithms are essential in the modern world to tackle with ever-changing
environmental disturbances while meeting adequate safety standards. Control systems must …

A novel adaptive model for a recently devised distributed Differential Evolution algorithm is
introduced. The distributed algorithm, following the stepping-stone model, is characterized …

For smart building applications, identifying and tracking the objects and people in and
around a building is an inevitable problem. There exist many approaches for solving this …

This chapter proposes to present the design and development of an evolutionary
computation (EC) based system which solves two interesting real-world problems. In the first …

Differential evolution (DE) is a real parameter optimisation algorithm added to the pool of
algorithms under evolutionary computing field. DE is well known for simplicity and …