A genetic local search algorithm for multiobjective time-dependent route planning

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

The combination of local search heuristics and genetic algorithms has been shown to be an effective approach for finding near-optimum solutions to the traveling salesman problem. Previously proposed genetic local search algorithms for the symmetric and asymmetric traveling salesman problem are revisited and potential improvements are identified. Since local search is the central component in which most of the computation time is spent, improving the efficiency of the local search operators is crucial for improving the overall performance of the algorithms. The modifications of the algorithms are described and the new results obtained are presented. The results indicate that the improved algorithms are able to arrive at better solutions in significantly less time.

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.

A novel Hybrid Genetic Local Search Algorithm for feature selection and weighting with an application in strategic decision making in innovation management

In this paper, based on the phase-position perturbation method, an innovative optimal adaptive antenna technique is proposed, where the deduced radiation pattern formulas available for searching optimal solutions are used to search the optimal weighting vector. The optimal radiation pattern designs of adaptive antenna are studied by the phase-position perturbation method. Memetic algorithms are used to search the optimal weighting vector of the phase-position perturbations for the array factor. The design for an optimal radiation pattern of an adaptive antenna can not only adjustably suppress the interferers by placing nulls at the directions of the interfering sources, but at the same time provide a maximum main lobe in the direction of the desired signal, i.e., to maximize the signal-to-interference ratio. To achieve this goal, a new convergent method, referred to as the two-way convergent method for memetic algorithms, is proposed. The memetic algorithm combines a genetic algorithm and local search heuristics to solve combinatorial optimization problems. The memetic algorithm is a kind of improved type of the traditional genetic algorithm. By using a local search procedure, it can avoid the shortcomings of the traditional genetic algorithm, whose termination criteria are set up by using the trial and error method. This proposed method is also able to solve the multipath problem, which exists at the same time in this communication system. The optimal radiation pattern concept can be implemented in practical wireless communication systems. Simulation results are also given in this paper.

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.

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.

In the paper we present a new method for solving optimal control problems of a class of hybrid systems. We describe the new effective algorithm based on memetic algorithm (MA) for optimal control of switched systems. We concentrate on a class of problems in which a pre-specified sequence of active subsystems is known. Our aim is to find both the optimal switching instants and the optimal continuous inputs. The new approach, which we propose, decomposes the cost functional of the basic optimal control problem in Bolza form in two terms. The first term depends explicitly on a value of state variables at the final time. The second term depends on state and control trajectories. In oreder to solve those two tasks we used MA as the multi-objective optimization algorithm. In this paper we considered a fundamental bi-criteria case with two mentioned before functions: the value of state variables at the final time and the state and control trajectories. In order to find an approximation of Pareto frontier, we proposed new effective method based on genetic algorithm (GA) and local search (LS). Problems properties were taken into consideration in the design of our new approach of solving it. They were used to construct new algorithm inspired by the LS NSGA-II, which performed rather well in multi-criteria scheduling problem. Since simple genetic algorithms are efficient heuristics in searching for optimal solutions, but lack the accuracy of some more computational complex algorithms, a hybrid algorithm was constructed. It uses fast non-dominated sorting, in order to evaluate child population and allocate solutions to corresponding Pareto frontiers. I addition a local search method was used, in order to find more differentiated and better solutions. Clustering solutions from Pareto frontiers also improved diversity of solutions in child population. This approach can be used as a start point for searching for algorithm for solving optimal control problems of switched systems without pre-specified sequence of active subsystems. The performance of the algorithm is illustrated by the examples: (a) a hybrid optimal control problem with nonlinear dynamics and (b) a switched linear quadratic optimal control problem with long switching time intervals.

This paper introduces a genetic local search algorithm for bi­ nary constraint satisfaction problems. The core of the algo­ rithm consists of an ad-hoc optimization procedure followed by the application of blind genetic operators. A standard set of benchmark instances is used in order to assess the performance of the algorithm. The results indicate that this apparently naive hybridation of a genetic algorithm with lo­ cal search yields a rather powerful heuristic algorithm for random binary constraint satisfaction problems.

Genetic local search for multi-objective combinatorial optimization

The genetic algorithm BIANCA, developed for design and optimisation of composite laminates, is a multi-population genetic algorithm, capable to deal with unconstrained and constrained hard combinatorial optimisation problems in engineering. The effectiveness and robustness of BIANCA rely on the great generality and richness in the representation of the information, i.e. the structure of populations and individuals in BIANCA, and on the way the information is extensively exploited during genetic operations. Moreover, we developed proper and original strategies to treat constrained optimisation problems through the generalisation of penalisation methods. BIANCA can also treat constrained multi-objective problems based on the construction of the Pareto frontier. Therefore, BIANCA allows us to approach very general design problems for composite laminates, but also to make a step forward to the treatment of more general problems of optimisation of materials and structures. In this paper, we describe specifically the case of optimal design of composite laminates, concerning both the theoretical formulation and the numeric resolution.

The complexity of closed-loop supply chain makes the operation of logistics system more dependent on logistics network, so the facility location-allocation problem in closed-loop supply chains has been the most important problem in the supply chain management with reverse logistics. The Facility location-allocation problem in designing closed-loop supply chains is analyzed in this paper at first, and then the mathematical model is formulated. In the model the logistics network is shared by forward logistics and reverse logistics, and the demanded quantities is considered as stochastic variable. The objective of the model is to optimize the total cost of the closed-loop supply chain. Because the formulated model is NP-hard, genetic local search algorithm is designed to solve the problem. The local search is used in the algorithm to improve the search effectiveness. Simulations based on numerical examples show that the proposed mathematical model and the algorithm are effective.

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.

This paper presents the results of an exploratory study aimed at investigating the impacts of optimisation of PV plant locations in South Africa in the context of the seasonal and diurnal cycles associated with the system load profiles and solar generation profiles. The distribution of a normalised per-unit generation capacity is optimised across a set of locations chosen to represent the diurnal cycle along a west-east axis and the seasonal cycle along a north-south axis, using local solar irradiance profiles for the candidate sites. Optimisations are conducted for a range of objective functions representing different scenarios defined in terms of seasonal considerations and time-of-use (TOU) periods using both genetic algorithm (GA) and pattern search methods in the MATLAB simulation environment. Comparative analysis of the optimisation results indicate that the latitude of PV plant locations plays a significant role in seasonal performance, while optimisation along a longitude dimension offers higher power generation during daily peak demand periods.

Vehicle Routing Problem with Time windows (VRPTW) is an example of scheduling in constrained environment. It is a well known NP hard combinatorial scheduling optimization problem in which minimum number of routes have to be determined to serve all the customers within their specified time windows. So far different analytic and heuristic approaches have been tried to solve such problems. In this paper we proposed algorithms which incorporate new local search techniques with genetic algorithm approach to solve VRPTW scheduling problems in various scenarios.