Genetic Local Search Algorithm Research Articles

A hybrid multi-objective genetic local search algorithm for the prize-collecting vehicle routing problem

This paper studies the prize-collecting vehicle routing problem (PCVRP), which is a new variant of the vehicle routing problem. In the PCVRP, besides the vehicle assignment and visiting sequencing, customer selection also must be determined because the available vehicles are insufficient to visit all the customers. Two types of PCVRP are often encountered in the real world, namely the PCVRP in which the number of vehicles is predetermined (PCVRP-P) and that in which the number of vehicles is not predetermined (PCVRP-NP). In general, multiple optimization objectives are required to be considered in both the PCVRP-P and PCVRP-NP. Unlike the commonly used weighted-sum method, a Pareto-based evolutionary algorithm featuring a genetic algorithm combined with a local search strategy (denoted as HGLS) is proposed for solving the multi-objective PCVRP-P. Subsequently, based on the proposed HGLS, a decomposition strategy is designed to solve the multi-objective PCVRP-NP problem by decomposing it into multiple PCVRP-P problems. In order to determine the number of PCVRP-P problems in the decomposition strategy, a vehicle-number-range determination approach is proposed to obtain a tight upper bound and lower bound of the number of vehicles. The superiority of the proposed algorithm is demonstrated by conducting several experiments on the benchmark problems.

The Multiple Traveling Salesman Problem with Backup Coverage

We consider a variant of the classical Multiple Traveling Salesmen Problem in which the distance between any two vehicles is never greater than a fixed distance D. This new feature allows salesmen to help each other timely if an emergency happens, with an estimated backup response time related to D. To achieve this goal, a spatial and temporal synchronization is required, and it incorporates routes interdependence difficulties to be overcomed. A Genetic Algorithm and a Local Search Genetic Algorithm that embodies a Variable Neighborhood Descent procedure are proposed to solve this problem. Computational results are reported on modified benchmark instances taken from TSPLIB in order to exhibit prospects of the proposed algorithms. Through an analysis of results, the highly effective performance of our proposed Local Search Genetic Algorithm is shown in comparison to the classical Genetic Algorithm.

Genetic local search algorithm for a new bi-objective arc routing problem with profit collection and dispersion of vehicles

We present a new bi-objective arc routing problem in which routes must be constructed in order to maximize collected profit and a non linear dispersion metric. A dispersion metric calculated based on instantaneous positions, suitable to capture routing characteristics found when vehicles have to travel in hostile environments, is a novelty in the routing literature. The inherent combinatorial nature of this problem makes it difficult to solve using exact methods. We propose a Multi-objective Genetic Local Search Algorithm to solve the problem and compare the results with those obtained by a well known multi-objective evolutionary algorithm. Computational experiments were performed on a new set of benchmark instances, and the results evidence that local search plays an important role in providing good approximation sets. The proposed method can be adapted to other multi-objective problems in which the exploitation provided by local search may improve the evolutionary procedures usually adopted.

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

In some applications, one needs not only to determine the relevant features but also provide a preferential ordering among the set of relevant features by weights. This paper presents a novel Hybrid Genetic Local Search Algorithm (HGA) in combination with the k-nearest neighbor classifier for simultaneous feature subset selection and feature weighting, particularly for medium-sized data sets. The performance of the proposed algorithm is compared with the performance of alternative feature subset selection algorithms and classifiers through experimental analyses in the various benchmark data sets publicly available on the UCI database. The developed HGA is then applied to a data set gathered from 184 manufacturing firms in the context of innovation management. The data set consists of scores of manufacturing firms in terms of various factors that are known to influence the innovation performance of manufacturing firms and referred to as innovation determinants, and their innovation performances. HGA is used to determine the relative significance of the innovation determinants. Our results demonstrated that the developed HGA is capable of eliminating the irrelevant features and successfully assess feature weights. Moreover, our work is an example how data mining can play a role in the context of strategic management decision making.

A Novel Hybrid Genetic Local Search Algorithm for Feature Selection and Weighting with an Application in Strategic Decision Making in Innovation Management

In some applications, one needs not only to determine the relevant features but also provide a preferential ordering among the set of relevant features by weights. This paper presents a novel Hybrid Genetic Local Search Algorithm (HGA) in combination with the k-nearest neighbor classifier for simultaneous feature subset selection and feature weighting, particularly for medium-sized data sets. The performance of the proposed algorithm is compared with the performance of alternative feature subset selection algorithms and classifiers through experimental analyses in the various benchmark data sets publicly avail- able on the UCI database. The developed HGA is then applied to a data set gathered from 184 manufacturing firms in the context of innovation manage- ment. The data set consists of scores of manufacturing firms in terms of various factors that are known to influence the innovation performance of manufacturing firms and referred to as innovation determinants, and their innovation perfor- mances. HGA is used to determine the relative significance of the innovation determinants. Our results demonstrated that the developed HGA is capable of eliminating the irrelevant features and successfully assess feature weights. More- over, our work is an example how data mining can play a role in the context of strategic management decision making.

Optimization of linear consecutive-k-out-of-n system with a Birnbaum importance-based genetic algorithm

The optimization of linear consecutive-k-out-of-n (Lin/Con/k/n) is to find an optimal component arrangement where n components are assigned to n positions to maximize the system reliability. With the interchangeability of components in practical systems, the optimization of Lin/Con/k/n systems is becoming widely applied in engineering practice, which is also a typical component assignment problem concerned by many researchers. This paper proposes a Birnbaum importance-based genetic algorithm (BIGA) to search the near global optimal solution for Lin/Con/k/n systems. First, the operation procedures and corresponding execution methods of BIGA are described in detail. Then, comprehensive simulation experiments are implemented on both small and large systems to evaluate the performance of the BIGA by comparing with the Birnbaum importance-based two-stage approach and Birnbaum importance-based genetic local search algorithm. Thirdly, further experiments are provided to discuss the applicability of BIGA for Lin/Con/k/n system with different k and n. Finally, the case study on oil transportation system is implemented to demonstrate the application of BIGA in the optimization of Lin/Con/k/n system.

Vertex-coloring of fuzzy graphs: A new approach

In this paper, a new vertex-coloring problem of a fuzzy graph with crisp vertices and fuzzy edges is studied. Membership degree of a fuzzy edge is interpreted as incompatibility degree of its associated incident vertices. This interpretation can be used to define the concept of total incompatibilit y. Here, unlike the traditional graph coloring problems, two adjacent vertices can receive same colors; these type of vertices and their associated edge are named incompatible vertices and incompatible edge, respectively. In proposed coloring methodology, the total incompatibility of a vertex-coloring is defined as the sum of incompatibility degrees of all incompatible edges. Then, based on the minimum possible degree of total incompatibilities, fuzzy chromatic number of a fuzzy graph is introduced. In order to find an optimal k-coloring, with minimum degree of total incompatibly, firstly a binary programming problem is formulated. Then, a hybrid local search genetic algorithm is designed to solve the large-size problems. Practical uses of the proposed algorithm are illustrated and analyzed by different-size problems. Finally, a cell site assignment problem, as a real world application of the presented fuzzy graph vertex-coloring, is formulated and solved.

A local search genetic algorithm for the job shop scheduling problem with intelligent agents

The job shop scheduling problem is one of the most important and complicated problems in machine scheduling and is considered to be a member of a large class of intractable numerical problems known as NP-hard. Genetic algorithms have been implemented successfully in many scheduling problems, in particular job shop scheduling. Hybridization is an effective way of improving the performance and effectiveness of genetic algorithms. Local search techniques are the most common form of hybridization that can be used to enhance the performance of these algorithms. Agent-based systems technology has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. This paper presents an agent-based local search genetic algorithm for solving the job shop scheduling problem. A multi agent system containing various agents each with special behaviors is developed to implement the local search genetic algorithm. Benchmark instances are used to investigate the performance of the proposed approach. The results show that the proposed agent-based local search genetic algorithm improves the efficiency.

Function fitting the symmetric radiation pattern of a LED with attached secondary optic.

The analytical model [Moreno and Sun, Opt. Express 16, 1808-1819 (2008)] is applied to LED with attached secondary optics. It is shown that a slightly modified model using only three cosine power functions can be used for the cases with symmetric radiation pattern and that good fitting on realistic examples can be achieved with several standard optimization algorithms including local search heuristics and genetic algorithm.

Genetic Local Search Algorithm with Self-Adaptive Population Resizing for Solving Global Optimization Problems

In the past decades, many types of nature inspired optimization algorithms have been proposed to solve unconstrained global optimization problems. In this paper, a new hybrid algorithm is presented for solving the nonlinear unconstrained global optimization problems by combining the genetic algorithm (GA) and local search algorithm, which increase the capability of the algorithm to perform wide exploration and deep exploitation. The proposed algorithm is called a Genetic Local Search Algorithm with Self-Adaptive Population Resizing (GLSASAPR). GLSASAPR employs a self-adaptive population resizing mechanism in order to change the population size NP during the evolutionary process. Moreover, a new termination criterion has been applied in GLSASAPR, which is called population vector (PV) in order to terminate the search instead of running the algorithm without any enhancement of the objective function values. GLSASAPR has been compared with eight relevant genetic algorithms on fifteen benchmark functions. The numerical results show that the proposed algorithm achieves good performance and it is less expensive and cheaper than the other algorithms.

Genetic algorithm with local search for the unrelated parallel machine scheduling problem with sequence-dependent set-up times

In this paper, a genetic algorithm (GA) with local search is proposed for the unrelated parallel machine scheduling problem with the objective of minimising the maximum completion time (makespan). We propose a simple chromosome structure consisting of random key numbers in a hybrid genetic-local search algorithm. Random key numbers are frequently used in GAs but create additional difficulties when hybrid factors are implemented in a local search. The best chromosome of each generation is improved using a local search during the algorithm, but the better job sequence (which might appear during the local search operation) must be adapted to the chromosome that will be used in each successive generation. Determining the genes (and the data in the genes) that would be exchanged is the challenge of using random numbers. We have developed an algorithm that satisfies the adaptation of local search results into the GAs with a minimum relocation operation of the genes’ random key numbers – this is the main contribution of the paper. A new hybrid approach is tested on a set of problems taken from the literature, and the computational results validate the effectiveness of the proposed algorithm.

Parameter Identifiability and Parameter Estimation of a Diesel Engine Combustion Model

In this paper an original method based on the link between a piecewise identifiability analysis and a piecewise numerical estimation is presented for estimating parameters of a phenomenological diesel engine combustion model. This model is used for design, validation and pre-tuning of engine control laws. A cascade algebro-differential elimination method is used for studying identifiability. This investigation is done by using input-output-parameter relationship. Then these relations are transformed by using iterated integration. They are combined with an original numerical derivative estimation based on distribution theory which gives explicit point-wise derivative estimation formulas for each given order. Then new approximate relations, linking block of parameters and outputs (without derivative) are obtained. These relations are linear relatively to the blocks of parameters and yield a first estimation of parameters which is used as initial guess for a local optimization method (least square method and a local search genetic algorithm).

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature.

A Research on Methane Prediction Model Based on Improved BP-GA Network

The amount of methane emission is crucial to the safety of coal mine. The paper proposes the Levenberg Marquardt (LM) algorithm (in the nonlinear least squares algorithms) that can reduce the training time of BP network. Genetic Algorithm (GA) is used to optimize weights in global search to prevent the inherent defects that neural network is liable to get stuck in local minimal points. Furthermore, neural network can prevent the defect of weak GA local search. Finally, BP-GA modal was trained and the sample data were precisely analyzed, which proves that this model features broad adaptability and precision.

A genetic local search algorithm for the multi-depot heterogeneous fleet capacitated arc routing problem

This paper studies the multi-depot heterogeneous fleet capacitated arc routing problem (MDHCARP), a problem with rare research in the past, but with many applications in real life. The MDHCARP extends the capacitated arc routing problem (CARP) by considering both the multi-depot case and limited heterogeneous fleet constraints. We propose a genetic local search (GLS) algorithm for the MDHCARP. The GLS is appraised on simplified MDHCARP cases and on general MDHCARP instances from CARP files; and computational results show that the GLS outperforms an extended memetic algorithm and meanwhile they both improve best-known solutions of the simplified MDHCARP benchmark cases.

A Birnbaum-importance based genetic local search algorithm for component assignment problems

This paper considers the component assignment problem (CAP) of finding the optimal assignment of n available components to n positions in a system such that the system reliability is maximized. To solve the CAP, an important type of problems in reliability, we propose a Birnbaum-importance based genetic local search (BIGLS) algorithm in which a local search using the Birnbaum importance is embedded into the genetic algorithm. This paper presents comprehensive numerical tests to compare the performance of the BIGLS with a general genetic algorithm and a Birnbaum-importance based two-stage heuristic. The testing results show that the BIGLS is robust (with respect to its random operations) and effective, and outperforms two benchmark methods in terms of solution quality. It demonstrates the effectiveness of embedding the Birnbaum importance in the local search under the genetic evolutionary mechanism.

A Genetic Local Search Algorithm based on Insertion Neighborhood for the Job Shop Scheduling Problem

A Genetic Local Search Algorithm based on Insertion Neighborhood for the Job Shop Scheduling Problem

A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. Iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context, we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately, 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches, as well.

An asynchronous genetic local search algorithm for the permutation flowshop scheduling problem with total flowtime minimization

In this study, the permutation flowshop scheduling problem with the total flowtime criterion is considered. An asynchronous genetic local search algorithm (AGA) is proposed to deal with this problem. The AGA consists of three phases. In the first phase, an individual in the initial population is yielded by an effective constructive heuristic and the others are randomly generated, while in the second phase all pairs of individuals perform the asynchronous evolution (AE) where an enhanced variable neighborhood search (E-VNS) as well as a simple crossover operator is used. A restart mechanism is applied in the last phase. Our experimental results show that the algorithm proposed outperforms several state-of-the-art methods and two recently proposed meta-heuristics in both solution quality and computation time. Moreover, for 120 benchmark instances, AGA obtains 118 best solutions reported in the literature and 83 of which are newly improved.