ABSTRACT

In artificial intelligence, evolutionary algorithms (EAs) have shown to be effective and 
robust in solving difficult optimization problems. EAs are generic population-based 
metaheuristic optimization algorithms. The mechanisms used in EAs are inspired by biological 
evolution: reproduction, mutation, recombination, and selection. The development of EAs can 
be classified into two categories: single objective and multi-objective optimization. In 
this thesis, both single objective and multi-objective evolutionary algorithms have been 
studied. For single objective optimization, various niching techniques are integrated with 
differential evolution (DE) and particle swarm optimization (PSO) for multi-modal optimization. 
Multi-modal optimization deals with optimization tasks that involve finding all or most of 
the global/local peaks in one single run. EAs in their original forms are usually designed 
for locating one single global solution. To promote and maintain formation of multiple stable 
subpopulations within a single population, we introduced a neighborhood mutation technique to 
enhance DE with ability of handling multi-modal problems. We also proposed a locally informed 
PSO to tackle multi-modal optimization. Beside these, several existing niching techniques from 
the literature were modified and improved by us. For multi-objective evolutionary algorithms, 
we proposed a summation of normalized objective values and diversified selection (SNOV-DS) 
method to replace the classical non-domination sorting. The process of classical non-domination 
sorting is complex and time consuming. By use of the proposed method, not only the simulation 
speed is increased, but also the performance of the algorithm is improved. We also introduced 
an ensemble of constraint handling methods (ECHM) to solve constrained multi-objective 
optimization problems, where each constraint handling method had its own population. ECHM 
allows different constraint handling methods to generate offspring and exchange information. 
In this way, the offspring produced by the most suitable constraint handling method will survive 
and be set as parents for next generation. Lastly, we applied the proposed algorithm to solve 
environmental/economic power dispatch problem. We demonstrated the superior performance of the 
proposed algorithm over other similar evolutionary algorithms reported in literature.