Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection

Abstract

Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

“…Simulated binary crossover (SBX) is one of the main recombination operators used in various real-coded, multi-objective optimization algorithms (Deb et al 2002;Gong et al 2008). In this paper, the SBX operation is performed on the population before and after the application of the proportional clonal operation, which is similar to the operation used in Deb et al, 2002 andGong et al, 2008. The operation can be described as follows:…”

“…By using non-dominated neighbor-based selection and proportional cloning, the method enhances the local search results in the lesscrowded regions of the current trade-off front. The experimental study showed that NNIA converges on the true Pareto optimal fronts when solving most test problems and is much better than the other algorithms, such as SPEA2, NSGA-II, PESA-II, and MISA (Gong and Jiao 2008). However, the probability of being trapped in the local optima of multimodal problems when using this algorithm is increased significantly.…”

“…Zhang (2007) also proposed an immune-algorithm for solving constrained nonlinear multi-objective optimization problems and reported that his algorithm performed well when compared to well-known evolutionary algorithms, such as MISA and SPEA. Gong et al (2008) proposed a non-dominated neighbor-based immune algorithm (NNIA) for solving unconstrained MOPs by using a novel non-dominated neighborbased selection technique, proportional cloning, heuristic search operators and elitism. In NNIA, the selection technique only performs on non-dominated individuals and selects minority-isolated individuals to clone proportionally to the crowding distance values, recombine and mutate.…”

Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

“…In the past 10 years, a number of artificial immune model-based multi-objective optimization algorithms have been proposed, including the evolutionary multi-objective immune algorithm [45], the non-dominated neighbor immune algorithm [51], the weight-based multi-objective artificial immune system [52], and the novel immune clonal algorithm (NICA). Experiments have demonstrated that among the existing algorithms, the quality of non-dominated solution sets obtained using the NICA is, in most cases, far superior to that obtained using other algorithms [32].Therefore, the present study uses the NICA as an essential method for solving the multi-objective LUA optimization problems.…”

A General Framework for Multi-Objective Optimization Immune Algorithms

“…Based on this, we implement four classical MO-AIS algorithms, CLONA LG [11], NNIA [12], MISA [13] and CM OIA [14], to show the validity of this framework. …”