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Abstract: In this paper, a novel multi-objective group search optimizer named NMGSO is proposed for solving the multi-objective optimization problems. To simplify the computation, the scanning strategy of the original GSO is replaced by the limited pattern search procedure. To enrich the search behavior of the rangers, a special mutation with a controlling probability is designed to balance the exploration and exploitation at different searching stages and randomness is introduced in determining the coefficients of members to enhance the diversity. To handle multiple objectives, the non-dominated sorting scheme and multiple producers are used in the algorithm. In addition, the kernel density estimator is used to keep diversity. Simulation results based on a set of benchmark functions and comparisons with some methods demonstrate the effectiveness and robustness of the proposed algorithm, especially for the high-dimensional problems. [Copyright &y& Elsevier]

Abstract: This paper presents an alternative approach to the dual response systems problem by utilizing a tabu search algorithm that yields a string of solutions and examine the trade-offs graphically and systematically how the controllable variables simultaneously impact the mean and the standard deviation of a characteristic of interest relevant to an industrial process. Heuristic-based search techniques may be very useful for cases where interactive multi-objective optimization techniques are not available due to lack of willingness of decision-makers. A further advantage of tabu search is its simplicity and we show that the entire process only occupies a few lines of codes and generates string of solutions in speedy manner especially for the larger-the-better/smaller-the-better cases of Taguchi’s robust parameter design. The procedure is illustrated with an example. [Copyright &y& Elsevier]

Abstract: Research in assembly planning can be categorised into three types of approach: graph-based, knowledge-based and artificial intelligence approaches. The main drawbacks of the above approaches are as follows: the first is time-consuming; in the second approach it is difficult to find the optimal solution; and the third approach requires a high computing efficiency. To tackle these problems, this study develops a novel approach integrated with some graph-based heuristic working rules, robust back-propagation neural network (BPNN) engines via Taguchi method and design of experiment (DOE), and a knowledge-based engineering (KBE) system to assist the assembly engineers in promptly predicting a near-optimal assembly sequence. Three real-world examples are dedicated to evaluating the feasibility of the proposed model in terms of the differences in assembly sequences. The results show that the proposed model can efficiently generate BPNN engines, facilitate assembly sequence optimisation and allow the designers to recognise the contact relationships, assembly difficulties and assembly constraints of three-dimensional (3D) components in a virtual environment type. [Copyright &y& Elsevier]