Inter-molecular Ineffective Collision Research Articles

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

The shortest common supersequence (SCS) problem is a classical NP-hard problem, which is normally solved by heuristic algorithms. One important heuristic that is inspired by the process of chemical reactions in nature is the chemical reaction optimization (CRO) and its algorithm known as CRO_SCS. In this paper we propose a novel CRO algorithm, dubbed IMCRO, to solve the SCS problem efficiently. Two new operators are introduced in two of the four reactions of the CRO: a new circular shift operator is added to the decomposition reaction, and a new two-step crossover operator is included in the inter-molecular ineffective collision reaction. Experimental results show that IMCRO achieves better performance on random and real sequences than well-known heuristic algorithms such as the ant colony optimization, deposition and reduction, enhanced beam search, and CRO_SCS. Additionally, it outperforms its baseline CRO_SCS for DNA instances, averaging a SCS length reduction of 1.02, with a maximum length reduction of up to 2.1.

GRSA Enhanced for Protein Folding Problem in the Case of Peptides

Protein folding problem (PFP) consists of determining the functional three-dimensional structure of a target protein. PFP is an optimization problem where the objective is to find the structure with the lowest Gibbs free energy. It is significant to solve PFP for use in medical and pharmaceutical applications. Hybrid simulated annealing algorithms (HSA) use a kind of simulated annealing or Monte Carlo method, and they are among the most efficient for PFP. The instances of PFP can be classified as follows: (a) Proteins with a large number of amino acids and (b) peptides with a small number of amino acids. Several HSA have been positively applied for the first case, where I-Tasser has been one of the most successful in the CASP competition. PEP-FOLD3 and golden ratio simulated annealing (GRSA) are also two of these algorithms successfully applied to peptides. This paper presents an enhanced golden simulated annealing (GRSA2) where soft perturbations (collision operators), named “on-wall ineffective collision” and “intermolecular ineffective collision”, are applied to generate new solutions in the metropolis cycle. GRSA2 is tested with a dataset for peptides previously proposed, and a comparison with PEP-FOLD3 and I-Tasser is presented. According to the experimentation, GRSA2 has an equivalent performance to those algorithms.

Optimization of protein folding using chemical reaction optimization in HP cubic lattice model

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.

Chemical Reaction Multi-Objective Optimization for Cloud Task DAG Scheduling

Cloud computing systems often have two conflicting objective, maximizing service performance, and minimizing computing cost. The excellent task scheduling and resource allocation strategies can improve the cost/utility ratio efficiently. It is an NP-hard problem to optimize task scheduling of precedence-constrained parallel tasks represented by a directed acyclic graph (DAG) on the cloud system. In order to address this problem, a chemical reaction multi-objective optimization algorithm (CRMO) is proposed in this paper. The CRMO executes four chemical reaction operators (named on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis) for cloud tasks DAG scheduling. The experimental results show that CRMO can produce outstanding cloud task scheduling solutions set.

Biobjective Optimal Reactive Power Flow Using Chemical Reaction Based Optimization

The optimal reactive power flow (ORPF) helps to effectively utilize the existing reactive power source with a view to minimize the network loss and improve voltage profile. The chemical reaction optimization (CRO), inspired from the interactions of molecules in a chemical reaction to reach a low energy stable state and searches for optimal solution through reactions involving the on-wall ineffective collisions, decomposition, inter-molecular ineffective collision and synthesis. This paper attempts to develop a global best solution method that simultaneously minimizes the network loss and enhances the voltage profile for ORPF using CRO. The results of IEEE 30 bus system are presented to demonstrate its performance.

A decomposition-based chemical reaction optimization for multi-objective vehicle routing problem for simultaneous delivery and pickup with time windows

A practical variant of the vehicle routing problem (VRP), with simultaneous delivery and pickup and time windows (VRPSDPTW) is a challenging combinatorial optimization problem that has five optimization objectives in transportation and distribution logistics. Chemical reaction optimization has been used to solve mono and multi-objective problems. However, almost all attempts to solve multi-objective problems have included continues problems less than four objectives. This paper studies discrete multi-objective VRPSDPTW using decomposition-based multi-objective optimization chemical reaction optimization. In the proposed algorithm, each decomposed sub-problem is represented by a chemical molecule. All of the molecules are divided into a few groups, with each molecule having several neighboring molecules. To balance the diversity and convergence, we designed operators of on-wall ineffective collision and inter-molecular ineffective collision for a local search, as well as operators of decomposition and synthesis to enhance global convergence. The proposed approach is compared with two different algorithms on hypervolume performance measures. Experimental results show that the proposed algorithm outperform the other algorithms in most benchmarks.

Adaptive chemical reaction based spatial fuzzy clustering for level set segmentation of medical images

Abstract The chemical reaction optimization (CRO), inspired from the interactions of molecules during chemical reactions in reaching a low energy stable state, searches for optimal solution through simulated reactions involving the on-wall ineffective collisions, decomposition, inter-molecular ineffective collision and synthesis. This paper attempts to obtain the global best centroids for spatial fuzzy clustering (SFC) using adaptive CRO (ACRO) with a view to facilitate the level set segmentation for medical images. The approach helps to analyze tumors or unhealthy region in various medical images. The results of medical images of brain, liver, abdomen and eye images are presented to demonstrate the performance.

Decomposition-based chemical reaction optimization (CRO) and an extended CRO algorithms for multiobjective optimization

Multiobjective optimization is an essential and challenging topic in the domains of science and engineering optimization. An effective decomposition-based chemical reaction optimization metaheuristic algorithm (MOCRO/D) for solving multiobjective optimization problems (MOPs) is proposed in this paper. In the proposed algorithm, each decomposed sub-problem is represented by a chemical molecule. All molecules are divided into a few groups and each molecule has several neighboring molecules. During the search, each molecule records its potential energy (optimal solution), the hits of number and the kinetic energy. MOCRO/D has four operators, i.e., on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis. To explore the search space efficiently, polynomial mutation and multiple molecule collision operators are introduced in the inter-molecular collision and synthesis operator. The inter-molecular ineffective collision operator uses three molecules to collide, which can improve the information interacting with each other with a certain probability. The extended synthesis operator could increase the global search ability. Extended operators for MOCRO/D (MOECRO/D) are proposed that can better optimize the variables related problems. The proposed approach is compared with other metaheuristic algorithms available in the specialized literature. Results indicate that the proposed approach is competitive in most of the test instances.

Optimal location of UPFC controller in transmission network using hybrid chemical reaction optimization algorithm

This paper presents a new hybrid chemical reaction optimization (HCRO) approach which is based on chemical reaction optimization (CRO) and differential evolution (DE) to find the optimal placement and parameter setting of unified power flow controller (UPFC) to achieve optimal performance of power system network. In the proposed algorithm, four elementary reactions, i.e., on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis, are developed. Moreover, mutation operation of DE is integrated with inter-molecular ineffective collision and crossover operation is introduced in the inter-molecular collision, synthesis, and decomposition process to accelerate the convergence speed and improve the solution quality of CRO algorithm. Here, three different single objectives namely, minimization of the overall cost, transmission loss, voltage deviation and one multi-objective which simultaneously minimizes the transmission loss and voltage deviation are used. To verify the effectiveness, the proposed HCRO approach is implemented on IEEE 14-bus and IEEE 30-bus power systems. Moreover, to establish the superiority, the simulation results of the proposed HCRO technique are compared to the CRO and other previously reported algorithms published in the literature such as genetic algorithm (GA), particle swarm optimization (PSO), immune GA (IGA), immune PSO (IPSO) and hybrid immune algorithm (HIA). It is found that the results obtained by the proposed HCRO technique are superior to those obtained by other discussed algorithms.

Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities

Abstract This paper proposes a hybrid chemical-reaction optimization (HCRO) algorithm for solving the job-shop scheduling problem with fuzzy processing time. The flexible maintenance activities under both resumable and non-resumable situations are also considered to make the problem more close to the reality. In the proposed algorithm, each solution is represented by a chemical molecule. Four elementary reactions, i.e., on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis, are imposed. A well-designed crossover function is introduced in the synthesis and decomposition operators. In order to balance the exploitation and exploration, HCRO divides the evolution phase into two loop bodies: the first loop body contains on-wall ineffective collision and inter-molecular ineffective collision, while the second loop body includes all the four elementary reactions. Tabu search (TS) based local search is embedded in the proposed algorithm to enhance the convergence capability. A novel decoding approach is utilized to schedule each operation, while considering each flexible preventive maintenance activity on each machine. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed HCRO algorithm is shown against three efficient algorithms from the literature, i.e., SMGA ( Sakawa and Mori, 1999 ), GPSO ( Niu et al., 2008 ), and RKGA ( Zheng et al., 2010 ).

Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity

This paper proposes an effective discrete chemical-reaction optimization (DCRO) algorithm for solving the flexible job-shop scheduling problems with maintenance activity constraints. Three minimization objectives—the maximum completion time (makespan), the total workload of machines and the workload of the critical machine are considered simultaneously. In the proposed algorithm, each solution is represented by a chemical molecule. Four improved elementary reactions, i.e., on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis, are developed. A well-designed crossover function is introduced in the inter-molecular collision, synthesis, and decomposition operators. Tabu search (TS) based local search is embedded in DCRO to perform exploitation process. In addition, the decoding mechanism considering the maintenance activity is presented. Several neighboring approaches are developed to improve the local search ability of the DCRO. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed DCRO algorithm is shown against the best performing algorithms from the literature.