This work presents a multi-objective optimization method based on the artificial bee colony,
called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid …

This paper presents an efficient multi-objective improved teaching–learning based
optimization (MO-ITLBO) algorithm for solving multi-objective optimization problems. The …

Teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm
based on the natural phenomenon of teaching-learning process. In the present work, multi …

The present work proposes a multi-objective improved teaching-learning based optimization
(MO-ITLBO) algorithm for unconstrained and constrained multi-objective function …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …

In the present information age, data and information are vital not just for the survival of any
corporate entity, but also to provide it with an edge over its competitors. Data warehouses …

Multi-objective evolutionary algorithms (MOEAs) are currently a dynamic field of research
that has attracted considerable attention. Mutation operators have been utilized by MOEAs …

Economists in the post-industrial era had long realised that data, information and knowledge
are the key capital of any organisation. Presently, almost every enterprise maintains their …

Evolutionary algorithms for multiobjective problems utilize three types of operations for
progressing toward the higher fitness regions of the search space. Each type of operator …

Bee Colony optimization techniques are inspired by the high level of mutual intelligence
shown by the natural bees in the food foraging process. It is a population based natural …