الفهرس يوجد فقط 14 صفحة متاحة للعرض العام
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المستخلص
Many real-world problems involve two different types of problem difficulties: 1) multiple, conflicting objectives and 2) a highly complex search space. On the one hand, instead of a single optimal solution, competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Thus in recent years, Artificial Intelligent Optimization Techniques have been a growing interest for solving such complex problems. During the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI).There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some of methods are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts–computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AIS’s try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the AIS is emerging as an active and attractive field involving models, techniques and applications of great diversity.
A hybrid artificial intelligent approach based on the clonal selection
principle of AIS and Neural Networks (NN) is proposed to solve multiobjective
programming problems. Due to the sensitivity to the initial values
of initial population of antibodies (Ab’s), Neural networks is used to
initialize the boundary of the antibodies for AIS to guarantee that all the initial population of Ab’s is feasible. The proposed approach uses dominance principle and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the “not so good”
antibodies. A secondary (or external) population that stores the nondominated solutions found along the search process is used. Such secondary population constitutes the elitist mechanism of our approach and it
allows the secondary population to move towards the Pareto optimal front.
Although there are advantages to know the range of each objective for
Pareto-optimality and the shape of the Pareto-optimal frontier itself in a
problem for an adequate decision-making, the task of choosing a single
preferred Pareto optimal solution is also an important task. In this
dissertation, a Reference Point Based Multi-Objective Optimization Using
hybrid artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS) and Neural Networks is proposed. And,
instead of one solution, a preferred set of solutions near the reference points can be found.
Modified Multi-objective Immune System Algorithm (MMISA) is proposed with real parameters value not binary coded parameters, uniform
and non uniform mutation operator is applied to the clones produced.
MMISA deals with real values of problem variables so it works on continuous search space.