Multi-objective Optimization Problems (MOPs) are the processes of 
exploiting solutions to satisfy a set of objectives. Difficulties 
arise as there usually exist several objectives which are 
incommensurable or competing against each other. In consequence, 
for these problems there is no single optimal solution, but rather 
a set of alternatives to which no other solutions are superior in 
all the objectives simultaneously. These alternatives are known as 
<italic>Pareto-optimal</italic> solutions. In recent years, an 
ever-growing trend in solving MOPs is to exploit diversified and 
comprehensive <italic>Pareto optima</italic> which can constitute 
a complete knowledge base to meet various requirements. In such 
applications, Evolutionary Algorithms (EAs) have been widely adopted 
as powerful tools because of their population-based search approach 
and strong global optimization capability. This thesis focuses on 
<italic>designing effective and efficient Evolutionary Algorithms for
Multi-objective Optimization Problems</italic>. Our efforts are 
two-fold: (1) designing sequential Multi-Objective EAs with sound 
theoretical basis, and (2) designing parallel Multi-Objective EAs 
with strong scalability and speedup capability. The sequential 
algorithms (Chapters 5 and 8) are designed with emphases on both 
their Pareto optimization capability and, equally important, their 
stability concerns. For this purpose, several techniques (say, the
annealing-like controller and Coverage Quotient) are contrived. 
These techniques can one on hand enhance the algorithms' exploration 
strength and on the other hand ensure their persistent performance. 
In addition, on the basis of these techniques, a series of theories 
are established, which rigorously prove the convergence properties 
of these algorithms. The parallel algorithms (Chapters 6 and 7) 
achieve strong scalability and speedup capability by their full 
asynchronous collaboration strategies. In these algorithms, a 
two-level asynchronous adjustment operation in conjunction with a 
concise information exchange operation can provide precise and 
flexible harmonization amongst a set of distributed sub-processes, 
which in turn leads to superior scalability and speedup capability. 
The proposed algorithms are compared with some state-of-the-art
Multi-Objective EAs on a variety of benchmark problems. The comparison 
results show the success of our algorithms. Lastly, it should be 
noted that despite originally designed for the multi-objective
optimization purpose, some techniques presented in this thesis (say, 
the annealing-like controller and two-level adjustment operations) 
are not limited to the MOP domain. Instead, they are general-purpose 
operations which are applicable to other EA applications with minor 
or even without modifications.