The use of evolutionary algorithms for solving optimization problems has signi cantly grown during the past few years. Evolutionary algo- rithms draw inspiration from the process of natural evolution. Besides nitely terminating and iterative methods, evolutionary algorithms pro- vide approximate solutions to many optimization problems. Researchers have proposed many data structures and algorithms to solve complex problems e ciently but optimality has still challenges in case of multi- objective optimization problems. The other main challenge is to design lightweight evolutionary algorithms for live and energy-constrained ap- plications. In this thesis, three multi-objective evolutionary algorithms (A-MOCLPSO, CPSGA, and MOGCO) are proposed to address the above-mentioned challenges (optimality, lightweight algorithm, and en- ergy e ciency) and two real-world problems (network security harden- ing, and energy-e cient clustering in mobile ad-hoc networks) are solved using these algorithms. In particle swarm optimization (PSO), each particle uses its personal and global or local best positions by linear summation. However, it is very time consuming tond the global or local best positions in case of com- plex problems. To overcome this problem, a new multi-objective variant of PSO called Attributed Multi-objective Comprehensive Learning Par- ticle Swarm Optimizer (A-MOCLPSO) is proposed. In this technique, a randomly selected particle from the whole population is used to update the velocity of each dimension. This technique not only increases the speed of the algorithm but also searches in more promising areas of the search space. An extensive experimentation is performed on well-known benchmark problems to test the proposed algorithm. The experiments show very convincing results when the proposed algorithm is compared with existing algorithms available in the literature. The second algorithm proposed in this thesis is a variant of genetic al- gorithm (GA) called the comprehensive parent selection-based genetic algorithm (CPSGA). The proposed strategy selects di erent parents for each gene to generate new o spring. This strategy ensures diversity to discourage premature convergence. The proposed CPSGA algorithm is tested using the standard benchmark problems and the performance metrics taken from the literature and is also compared with the original Non-dominating Sorting Genetic Algorithm-II (NSGA-II). The results show signi cant improvement of CPSGA over NSGA-II and con rm that the proposed approach is a viable alternative to solve multi-objective op- timization problems. Group Counseling Optimizer (GCO) is a new heuristic inspired by hu- man behavior in problem solving during counseling within a group. GCO has been found to be successful in case of single-objective optimization problems, but so far it has not been extended to deal with multi-objective optimization problems. In this thesis, a Pareto dominance based GCO technique is presented in order to allow this approach to deal with multi- objective optimization problems. A self-belief-counseling probability op- erator has also been incorporated in the algorithm that enriches its ex- ploratory capabilities. As case studies, two problems related to communication networks are solved using multi-objective evolutionary algorithms; 1) Security hard- ening problem on an attack tree model of a networked system in order to optimize total security cost and residual damage, and provide diverse so- lutions for the problem, and 2) Pareto dominance based energy-e cient clustering in Mobile Ad hoc Networks (MANETs). To provide security and make the networking system more reliable, a number of e orts have been made by researchers for the past several years. Though many successful security systems have been designed and implemented, a number of issues such as time required for designing a secure system, cost, minimizing damage, and maintenance still need to be resolved. Designing the security system harder and avoiding unau- thorized access with a low cost simultaneously is a challenging task. Targeting such a multi-objective scenario, a few approaches have been applied previously to optimize the cost and the residual damage. In this thesis, this problem is solved using the proposed A-MOCLPSO, CPSGA, and MOGCO algorithms on an attack tree model of a networked system in order to optimize the total security cost and the residual damage. The performance of these algorithms is compared for the security hardening problem. A mobile ad hoc network (MANET) is dynamic in nature and is com- posed of wirelessly connected nodes that perform hop-by-hop routing without the help of anyxed infrastructure. One of the important re- quirements of a MANET is the e ciency of energy, which increases the lifetime of the network. Several techniques have been proposed by re- searchers to achieve this goal and one of them is clustering in MANETs that can help in providing an energy-e cient solution. In the literature, several optimization techniques are available for clustering that provide a single solution at a time. As a second case study, a multi-objective so- lution is proposed by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in an ad-hoc network as well as energy dissipation in nodes in order to provide an energy-e cient solution and reduce the network tra c. The results of the proposed approach are compared with two other well-known cluster- ing techniques, i.e., WCA and CLPSO-based clustering by using di erent performance metrics. The proposed MOPSO-based approach outper- forms these two algorithms innding optimal number of clusters as well as provides multiple options for the user.