Recently, one of the main tools of decision maker (DM) preference incorporation in the multiobjective optimization (MOO) has been using reference points and achievement scalarizing functions (ASF). The core idea of these methods is converting the original multiobjective problem (MOP) into single objective problem by using ASF to find a single preferred point. However, many DMs not only interest in a single point but also a set of efficient points in their preferred region. In this paper, we introduce a hybrid multiobjective immune algorithm (HMIA) for DM. It combines the immune inspired algorithm and region preference based on a novel dominance concept called region-dominance without ASF. The new algorithm can let DMs flexibly decide the number of reference points and accurately determine the preferred region with its simple and effective interactive methods. To exemplify its advantages, simulated results of HMIA are shown with some well-known problems.
[Show abstract][Hide abstract] ABSTRACT: In this paper, an interactive version of the decomposition based multiobjective evolutionary algorithm (iMOEA/D) is proposed for interaction between the decision maker (DM) and the algorithm. In MOEA/D, a multi-objective problem (MOP) can be decomposed into several single-objective sub-problems. Thus, the preference incorporation mechanism in our algorithm is implemented by selecting the preferred sub-problems rather than the preferred region in the objective space. At each interaction, iMOEA/D offers a set of current solutions and asks the DM to choose the most preferred one. Then, the search will be guided to the neighborhood of the selected. iMOEA/D is tested on some benchmark problems, and various utility functions are used to simulate the DM's responses. The experimental studies show that iMOEA/D can handle the preference information very well and successfully converge to the expected preferred regions.
13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July 12-16, 2011; 01/2011
[Show abstract][Hide abstract] ABSTRACT: During the two last decades, evolutionary algorithms have been successfully used to solve multiobjective optimization problems. Several works have been established to improve convergence and diversity. Recently, several multiobjective artificial immune systems have shown their ability to solve multiobjective optimization problems. However, in reality, decision makers are not interested with the whole optimal Pareto front rather than the portion of the Pareto front that matches at most their preferences, i.e., the region of interest. In this paper, we propose a new dominance relation inspired from several ideas of the danger theory, called Danger Zone-based dominance (DZ-dominance), which guides the search process towards the preferred part of the Pareto front. The DZ-dominance is incorporated within the Nondominated Neighbor Immune Algorithm (NNIA). The new preference-based algorithm, named DZ-NNIA, has demonstrated its ability to guide the search based on decision maker's preferences. Moreover, comparative experiments show that our algorithm outperforms the most recent preference-based immune algorithm HMIA and the preference-based multiobjective evolutionary algorithm g-NSGA-II.
2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI); 11/2012
[Show abstract][Hide abstract] ABSTRACT: In order to control the locomotive wheel (axle) load distribution, a shimming process to adjust the locomotive secondary spring loads was heretofore developed. An immune dominance clonal selection multi-objective algorithm based on the artificial immune system was presented to further improve the performance of the optimization algorithm for locomotive secondary spring load adjustment, especially to solve the lack of control on the output shim quantity. The algorithm was designed into a two-level optimization structure according to the preferences of the problem, and the priori knowledge of the problem was used as the immune dominance. Experiments on various types of locomotives show that owing to the novel algorithm, the shim quantity is cut down by 30%–60% and the calculation time is about 90% less while the secondary spring load distribution is controlled on the same level as before. The application of this optimization algorithm can significantly improve the availability and efficiency of the secondary spring adjustment process.
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