Unimodular Hypergraph for DNA Sequencing: A Polynomial Time Algorithm

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

The Job shop scheduling problem with generic time lags is a generalization of the job shop problem. It is defined as a job shop problem with minimal and maximal delays between starting times of operations of different jobs. In this paper, we propose a combination of genetic algorithm and tabu search to solve Job Shop Problem with Generic Time Lags. Benchmark instances are used to investigate the performance of the proposed metaheuristic. The results show that the proposed combination of metaheuristics improves the efficiency.

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

According to the individual and various demands of customers, establish single and mixed fleet single-depot vehicle routing problem with backhauls model. A hybrid heuristic algorithm is proposed to solving the problem, which combines the advantages of genetic algorithm and tabu search algorithm. Firstly, hybrid genetic algorithm is used to get the initialization solution. Namely, use natural number coding so as to simplify the problem; retain the best selection so as to guard the diversity of group, use essence cluster so as to generate more excellent chromosome. Secondly, the taboo searching algorithm is applied to optimizing the initialization solution, the best of which is taken by genetic algorithm. Consequently, the convergent speed and searching efficiency of algorithm are improved. Finally, the emulation and calculation prove that the hybrid heuristic algorithm is effective.

Equivalent circuit model of induction motors was established and the formulas of power losses were given. Motor efficiency was optimized by Genetic Algorithm(GA) and Hybrid Genetic Algorithm(HGA). Power factor, breakdown torque ratio, locked-rotor torque ratio and locked-rotor current ratio were regarded as constraints and the optimal result is satisfying. Genetic Algorithm and Pattern Search Algorithm are integrated into a composite algorithm-Hybrid Genetic Algorithm. Motor efficiency rises further through Hybrid Genetic Algorithm.

To let the economic and popular conventional Fresnel lens be available to a lighting system with multiple LEDs, we develop an efficient Genetic Algorithm to arrange LEDs' locations and a Tabu Search Algorithm to adjust their orientations to optimize illuminance and uniformity of a multiple-LED lighting system.

Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations

The binary defocusing technique has enabled speed breakthroughs for 3D shape measurement, yet simultaneously achieving high accuracy and high speed remains difficult. To overcome this limitation, this paper proposes a binary pattern optimization algorithm based on Genetic Algorithm and Tabu Search. Firstly, the relative phase is obtained by defocusing the binary pattern through the projector to avoid the influence of the gamma effect on the phase quality. Secondly, for reducing the influence of the projector's defocus on the Gray code phase unwrapping, a complementary combined Gray code method is proposed, which can accurately unwrap the phase. The experimental results show that the method in this paper improves the three-dimensional measurement results of the digital fringe projection technology and improves the robustness.

In a recent effort to push forward the powerful concept of software defined networks, Openflow has gained a lot of popularity as a practical approach to split the data and the control planes by standardizing an open interface that allow remote software controllers to dictate the forwarding behavior of network devices. This paper presents a TCAM resource allocation mechanism for the implementation of virtual Openflow v1.1 switch. This mechanism, based on optimization, aims to allocate the slice tables over the TCAM resources while minimizing the TCAM energy-consumption and maximizing the fairness between the slices. We formulate the problem as an integer non linear programming and show that his complexity is NP-complete. We solve it using Genetic algorithm and Tabu search. We compare our proposed algorithms and show that they provide near optimal solutions in short time. Our multi-objective problem offer the flexibility to the user to whether give preference to the lowest table allocation energy or the highest fairness between the slices.

In recent years, distributed generation, as clean natural energy generation and cogeneration system of high thermal efficiency, has increased due to the problems of global warming and exhaustion of fossil fuels. Many of the distributed generations are set up in the vicinity of the customer, with the advantage that this decreases transmission losses. However, output power generated from natural energy such as wind power, photovoltaics, etc, which is distributed generation, is influenced by meteorological conditions. Therefore when the distributed generation increases by conventional control techniques, it is expected that the voltage change of each node becomes a problem. Proposed in this paper is optimal control of distribution voltage with coordination of distributed installations, such as load ratio control transformer (LRT), step voltage regulator (SVR), shunt capacitor (SC), shunt reactor (ShR), and static var compensator (SVC). In this research, SVR is assumed to be a model with tap changing where the signal is received from a central control unit. Moreover, the communication infrastructure in the supply of the distribution system is assumed to be widespread. The technique proposed in this paper combines a genetic algorithm (GA) and Tabu search (TS) to determine the control operation. In order to confirm the validity of the proposed method, simulations are carried out for a the distribution network model with distributed (photovoltaic) generation.

The multiobjective time-dependent route planning problem is a hard multiobjective combinatorial optimization problem. Metaheuristics showed success in solving many hard optimization problems and recently many efforts have been directed to hybridize elements from different metaheuristics and search methods. The hybridization of genetic algorithms and local search methods proved to be successful in many domains. In this paper we present a genetic local search algorithm for solving the multiobjective time-dependent route planning problem taking the multiobjective route planning in dynamic multihop ridesharing as an example problem. The behavior of the proposed algorithm is compared, on two problem instances using a set of widely used quality indicators, with the behavior of a genetic algorithm proposed for solving the same problem. Experimentation results indicated that the proposed algorithm outperforms the genetic algorithm regarding all quality indicators.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

The aim of this study to demonstrate that the previously developed ANGEL algorithm can be efficiently used for multiple constrained sizing-shaping truss optimization problems. The applied hybrid method ANGEL, which was originally developed for simple truss optimization problems combines ant colony optimization (ACO), genetic algorithm (GA), and local search strategy (LS). ACO and GA search alternately and cooperatively in the solution space. In ANGEL, the traditional stochastic mutation operator is replaced by the local search procedure as a deterministic counterpart of the stochastic mutation. The feasibility is measured by the maximal load intensity factor computed by a third order path-following method. The powerful LS algorithm, which is based on the local linearization of the set of the constraints and the objective function, is applied to yield a better feasible or less unfeasible solution when ACO or GA obtains a solution. In order to demonstrate the efficiency of ANGEL in the given application area, a well-known example is presented under multiple constraints.