Previously, a meta-heuristic approach called Co-Operation of Biology Related Algorithms, or COBRA for short, based on a fuzzy logic controller for solving real-valued optimization problems was introduced and described. The basic idea of the originally proposed approach consists in a cooperative work of six well-known biology-inspired algorithms (components) with similar schemes. Furthermore, the fuzzy logic controller determines which biology-inspired algorithms should be included in the co-operative work and their population sizes at a given moment for solving optimization problems using the COBRA approach. In this study a new modification of the COBRA approach based on an alternative way of generating potential solutions is proposed. The stated technique uses a historical memory of successful positions found by individuals to guide them in different directions and thus to improve their exploration and exploitation abilities. The proposed method was applied to the components of the COBRA approach and to its basic procedures. The modified meta-heuristic as well as other variants of the COBRA algorithm and components (with and without the proposed modification) were evaluated on three sets of low- and high-dimensional benchmark problems. The experimental results obtained by all algorithms are presented and compared. It was concluded that the fuzzy-controlled COBRA with success-history based position adaptation allows better solutions to be found than the other mentioned biology-inspired algorithms with the same computational effort. Thus, the usefulness of the proposed position adaptation technique was demonstrated.

Biology-inspired algorithms are computationally efficient for real-parameter optimization. However, the search efficiency of such algorithms depends significantly on their ability in keeping the balance between exploration and exploitation when solving complex multimodal problems. A new technique for generating potential solutions in biology-inspired algorithms is proposed. The stated technique uses a historical memory of successful positions found by individuals to guide them in different directions, thereby improving their exploration and exploitation abilities. Thus, this paper describes the application of modified biology-inspired algorithms, namely the Firefly Algorithm, the Cuckoo Search Algorithm and the Bat Algorithm to global trajectory optimization problems. The problems are provided by the European Space Agency and represent trajectories of several well-known spacecraft, such as Cassini and Messenger. Firstly, modified versions of the listed heuristics as well as their original variants were evaluated on a set of various test functions. Then their performance was evaluated on two global trajectory optimization problems: Cassini-1 and Messenger. The experimental results obtained by them are presented and compared. It was established that success-history based position adaptation allows better solutions to be found with the same computational effort while solving complex real-world problems. Thus, the usefulness of the proposed position adaptation technique was demonstrated.

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In this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization problems. The basic idea consists of a cooperative work of six well-known biology-inspired algorithms, referred to as components. However, it was established that the search efficiency of COBRA depends on its ability to keep the exploitation and exploration balance when solving optimization problems. The new modification of the COBRA approach is based on other method for generating potential solutions. This method keeps a historical memory of successful positions found by individuals to lead them in different directions and therefore to improve the exploitation and exploration capabilities. The proposed technique was applied to the COBRA components and to its basic steps. The newly proposed meta-heuristic as well as other modifications of the COBRA approach and components were evaluated on three sets of various benchmark problems. The experimental results obtained by all algorithms with the same computational effort are presented and compared. It was concluded that the proposed modification outperformed other algorithms used in comparison. Therefore, its usefulness and workability were demonstrated.