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Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms. [ABSTRACT FROM PUBLISHER]