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The minimum vertex cover problem (MVCP) is one of the well-known NP-complete problems that can be used to formulate numerous real-life applications. The MVCP has been solved using different approaches, exact, heuristic, and metaheuristic. Chemical reaction optimization (CRO) is a population-based metaheuristic algorithm that simulates what happens in chemical reactions to solve many problems. In this paper, the MVCP is solved using hybridization of a modified version of the CRO algorithm and the best-first search (BFS) algorithm. This hybridization is symbolized by MCROA-BFS. The BFS algorithm is exploited to generate initial populations with high-quality initial solutions in comparison with the random bit-vector (RBV) approach as a traditional approach for generating initial populations for several population-based metaheuristic algorithms. At first, the MCROA is evaluated analytically in terms of run time complexity. Then the performance of the MCROA is evaluated experimentally to show the effectiveness of exploiting the BFS in terms of quality of gained solutions and run time. The most valuable player algorithm (MVPA), genetic algorithm (GA), and hybrid CRO algorithm (HCROA) are metaheuristic algorithms that used the RBV approach to generate the initial population. MCROA-BFS is compared, under the selected performance metrics, with these metaheuristic algorithms in addition to MCROA with the RBV approach (MCROA-RBV). The conducted experiments were carried out using 18 instances of DIMACS and BHOSLIB benchmarks. The experimental results revealed the superiority of MCROA-BFS over MVPA, GA, and MCROA-RBV in terms of both performance metrics. Although HCROA has the smallest run time, it failed to obtain high-quality solutions in comparison with MCROA. [ABSTRACT FROM AUTHOR]

Protein folding optimization is a very important and tough problem in computational biology. For solving this problem, a population-based metaheuristic algorithm named chemical reaction optimization (CRO) with HP cubic lattice model has been proposed in this paper. The proposed algorithm is combined with evolution and H&P compliance mechanisms which are responsible for increasing the performance of the algorithm. The evolution mechanism improves the performance of each individual solution. On the other hand, the H&P compliance mechanism tries to place the H monomer close to the center and place the P monomer as far as possible from the center of the related structure. The algorithm also applies four reactant operations of typical CRO algorithm decomposition, on-wall ineffective collision, synthesis and inter-molecular ineffective collision to solve the problem efficiently. The reactants or mechanisms may cause overlapping of the corresponding solutions. The algorithm also includes a repair mechanism which transforms invalid solutions into valid ones by removing overlapping in cubic lattice points. This algorithm has been tested over some sets of sequences and it shows very good performance. [ABSTRACT FROM AUTHOR]