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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

An improved chemical reaction optimization algorithm for solving the shortest common supersequence problem.

Fei Luo¹, Cheng Chen¹, Joel Fuentes²

¹School of Information and Engineering, East China University of Science and Technology, Shanghai, China.

Computational Biology and Chemistry

|July 21, 2020

Summary

This summary is machine-generated.

A new algorithm, IMCRO, efficiently solves the shortest common supersequence (SCS) problem. It introduces novel operators to chemical reaction optimization (CRO), outperforming existing methods, especially for DNA sequences.

Keywords:

Chemical reaction optimization Heuristic algorithm NP-hard Shortest common supersequence

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Area of Science:

Computational Biology
Bioinformatics
Algorithm Design

Background:

The shortest common supersequence (SCS) problem is a computationally challenging NP-hard problem.
Heuristic algorithms, including chemical reaction optimization (CRO), are commonly used to find approximate solutions.
The existing CRO_SCS algorithm is a notable heuristic for SCS.

Purpose of the Study:

To propose a novel and efficient algorithm, IMCRO, for solving the SCS problem.
To enhance the chemical reaction optimization (CRO) framework with new operators for improved SCS solutions.
To evaluate the performance of IMCRO against established heuristic algorithms.

Main Methods:

Introduction of two new operators: a circular shift operator in the decomposition reaction and a two-step crossover operator in the inter-molecular ineffective collision reaction.
Implementation of the Improved Chemical Reaction Optimization (IMCRO) algorithm based on these new operators.
Comparative experimental analysis on random and real-world sequences, including DNA instances.

Main Results:

IMCRO demonstrated superior performance compared to ant colony optimization, deposition and reduction, enhanced beam search, and CRO_SCS on various sequence types.
For DNA sequences, IMCRO achieved an average SCS length reduction of 1.02 compared to CRO_SCS.
A maximum SCS length reduction of up to 2.1 was observed with IMCRO on DNA instances.

Conclusions:

The proposed IMCRO algorithm offers an efficient and effective approach to solving the SCS problem.
The novel operators significantly enhance the performance of CRO for SCS, particularly for biological sequences.
IMCRO represents a valuable advancement in heuristic algorithms for sequence alignment and related bioinformatics challenges.