This dissertation focuses on developing a novel immune algorithm called for finding Pareto-optimal solutions simultaneously maintaining diversity to single- and multi-objective optimization problems (SOOPs and MOOPs) based fully on the features of a biological immune system. The applications in this dissertation include unconstrained/constrained test functions and truss-structure sizing multi-objective optimization, structural topology single-objective with multi-modally optimization, and single-objective job-shop scheduling optimization problems. The use of proposed immune algorithm as opposed to the evolutionary algorithm (e.g., genetic algorithm, GA, evolution strategy, ES) provides this methodology with superior diversification and local search abilities. Inter-relationships within the proposed algorithm resemble antibody-antigen relationships in terms of specificity and adaptiveness, antibody clonal proliferation, antigen discrimination, and the antibody memory characteristics of adaptive immune responses. Besides, the features for producing antibodies in biological immune system such as gene fragment rearrangement and several antibody diversification schemes (including somatic recombination, somatic mutation, gene conversion, gene reversion, gene drift, and nucleotide addition) are incorporated into the proposed immune algorithm in order to improve the balance between exploitation and exploration. Moreover the concept of cytokines is also combined to algorithm for constraint handling. By using several performance metrics and comparison with the other approaches, the effectiveness of proposed immune algorithm are evaluated by unconstrained/ constrained test functions and several engineering applications (truss sizing, structural topology, and scheduling). The simulated results demonstrated that the proposed immune algorithm provides better effect than other methods and suitable for searching in optimizations.