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Contact map overlap maximization using adaptive distributed modified extremal optimization

The detection of similar structures in proteins has received considerable attention in the post-genome era. Protein structure alignment, which is similar to sequence alignment, can detect the structural homology between two proteins according to their three-dimensional structures. One of the simplest yet most robust techniques for finding optimal protein structure alignment is to maximize the contact map overlap (CMO). This optimization is known as the CMO problem. We have been developing bio-inspired heuristic models using distributed modified extremal optimization (DMEO) for the CMO problem. DMEO is inspired by distributed genetic algorithms, which are known as island models. DMEO is a hybrid of population-based modified extremal optimization (PMEO) and the island model. In our previous work, we proposed a novel bio-inspired heuristic model, i.e., DMEO with different evolutionary strategies (DMEODES) to maintain population diversity. DMEODES is based on the island model; however, some of the islands, called hot-spot islands, have a different evolutionary strategy. In this paper, we propose a state-of-art heuristic model to improve the DMEO's ability to prevent evolution stagnation. The new model integrates an adaptive generation alternation mechanism in DMEO called ADMEO. To evaluate ADMEO, we used actual protein structures. Experimental results show that ADMEO outperforms DMEODES.