An Adaptive Coevolutionary Memetic Algorithm for Examination Timetabling Problems

A modified quasisecant method for global optimization

At present, bio-inspired geomagnetic navigation is mostly based on evolutionary strategy, which requires long navigation time and low efficiency. To solve this problem, a bio-inspired geomagnetic navigation method for AUV based on evolutionary gradient search is proposed. Combining the bionic evolutionary search algorithm with the classical gradient algorithm to search the function extremum can not only ensure the global optimization of the search, but also have fast convergence, which can improve the efficiency of bio-inspired geomagnetic navigation. The simulation results show that this method does not need prior geomagnetic information and can complete navigation tasks according to the geomagnetic trend. Comparing with the evolutionary search strategy, the effectiveness and superiority of the evolutionary gradient search strategy are verified.

The no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release times (RTs) is applicable in many areas, such as steel production, food processing, and chemical processing. Estimation of the distribution algorithm (EDA) has recently been recognized as a prominent metaheuristic methodology in the field of evolutionary computation due to its excellent performance of global exploration. In this article, an innovative matrix-cube-based (i.e., 3-D) EDA (MCEDA) is first proposed to minimize the total earliness and tardiness (TET) of the NFSSP with SDSTs and RTs. This problem is NP-hard in the strong sense. First, a 3-D matrix cube is devised to learn the valuable information from promising solutions or excellent individuals. Second, an EDA model or probabilistic model based on the matrix cube and a special sampling method is presented to perform effective exploration in solution space and find promising regions. Third, based on a series of newly defined subneighborhoods, a new local search with both a speed-up scanning method and one search strategy is developed to execute exploitation from promising regions. Fourth, a speed-up evaluation method based on the problem’s property is designed to reduce the computational complexity for calculating criterion and accelerate the search process. Owing to the reasonable hybridization of exploration and exploitation, MCEDA can perform very efficient search in solution space. Extensive test results on instances of such a just-in-time problem first show that MCEDA can achieve better solution than state-of-the-art algorithms in obviously less computation time. Additional experiments on instances of various NFSSPs further confirm the efficiency and robustness of MCEDA.

We present one general high-level hyper-heuristic approach for addressing two timetabling problems in the health care domain: the patient admission scheduling problem and the nurse rostering problem. The complex combinatorial problem of patient admission scheduling has only recently been introduced to the research community. In addition to the instance that was introduced on this occasion, we present a new set of benchmark instances. Nurse rostering, on the other hand, is a well studied operations research problem in health care. Over the last years, a number of problem definitions and their corresponding benchmark instances have been introduced. Recently, a new nurse rostering problem description and datasets were introduced during the first Nurse Rostering Competition. In the present paper, we focus on this nurse rostering problem description. The main contribution of the paper constitutes the introduction of a general hyper-heuristic approach, which is suitable for addressing two rather different timetabling problems in health care. It is applicable without much effort, provided a set of low-level heuristics is available for each problem. We consider the instances of both health care problems for testing the general applicability of the hyper-heuristic approach. Also, improvements to the previous best results for the patient admission scheduling problem are presented. Solutions to the new nurse rostering instances are presented and compared with results obtained by an integer linear programming approach.

The object of research is decision support systems. The subject of research is the decision-making process in management problems using the fish school (FSH) algorithm, an advanced genetic algorithm and evolving artificial neural networks. A solution search method using an improved FSH algorithm is proposed. The study is based on the FSH algorithm for finding a solution on the object state. For training FSH, evolving artificial neural networks are used. The method has the following sequence of actions: – input of initial data; – processing of initial data taking into account the degree of uncertainty; – checking the fitness function of the solution found; – procedure of feeding fish agents (FA); – instinctive-collective FA swimming; – calculation of the center of school gravity; – collective voluntary FA swimming; – changing the FA swimming parameters; – training of FA knowledge bases. The originality of the proposed method lies in the arrangement of FA taking into account the uncertainty of the initial data, improved global and local search procedures taking into account the degree of noise of data about the state of the analysis object. The peculiarity of the proposed method is the use of an improved FA training procedure. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. The use of the method makes it possible to increase the efficiency of data processing at the level of 18–25 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interest of solving national security problems

The object of research are decision support systems. The subject of research is the decision-making process in management problems using bio-inspired algorithms. A method for the search of solutions in the field of national security using bio-inspired algorithms is proposed. The proposed method is based on a combination of an artificial bat algorithm and evolving artificial neural networks. The method has the following sequence of actions: ‒ input of initial data; ‒ processing of initial data taking into account the degree of uncertainty; ‒ numbering of bat agents (BA); ‒ placement of bat agents taking into account the degree of uncertainty about the state of the analysis object in the search space; ‒ setting the initial BA speed and the echolocation frequency of each BA; ‒ starting a local search; ‒ launching a global search; ‒ training knowledge bases of bat agents. The originality of the proposed method consists in the arrangement of bat agents taking into account the uncertainty of initial data, improved global and local search procedures taking into account the noise degree of data about the state of the analysis object. Another feature of the proposed method is the use of an improved procedure for training bat agents. The training procedure consists in learning the synaptic weights of an artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 13–21 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems

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.

This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives--to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with the two-tailed Wilcoxon test for verifying the statistical significance of the results.

Recent years have witnessed great success of ant based hyper heuristics applying to real world applications. Ant based hyper heuristics intend to explore the heuristic space by traversing the fully connected graph induced by low level heuristics (LLHs). However, existing ant based models treat LLH in an equivalent way, which may lead to imbalance between the intensification and the diversification of the search procedure. Following the definition of meta heuristics, we propose an Ant based Hyper heuristic with SpAce Reduction (AHSAR) to adapt the search over the heuristic space. AHSAR reduces the heuristic space by replacing the fully connected graph with a bipartite graph, which is induced by the Cartesian product of two LLH subsets. With the space reduction, AHSAR enforces consecutive execution of intensification and diversification LLHs. We apply AHSAR to the p-median problem, and experimental results demonstrate that our algorithm outperforms meta heuristics from which LLHs are extracted.

Based on the success in the design of a new global search procedure on the development of a novel trail updating mechanism and the introduction of an elitist strategy to available ant colony optimization (ACO) methods, an improved ACO algorithm is proposed. In order to facilitate the implementation of the search procedure, the available local search phase is simplified also. The algorithm is tested on a mathematical function and an inverse problem, and its performances are compared with those of other well designed methods.

Office Space Allocation (OSA) is the task of correctly allocating the spatial resources of an institution to a set of entities by minimising the wastage of space and the violation of additional constraints. In this paper, an evolutionary local search algorithm is presented to tackle this problem. The evolutionary components of the algorithm include standard crossover and mutation operators and a relatively small population of individuals. The offspring produced by the evolutionary operators are subjected to a short but intense local search process. A very fast cost calculation method tailored for searching a large section of the search space is implemented. Extensive experimentation is carried out related to several parameters of the algorithm: the mutation rate, the population size, the length of the local search procedure after each mutation, hence the balance between the evolutionary and the local search stages, and the level of greediness of the local search process. The final results on 72 different data instances show that this hybrid evolutionary algorithm is very competitive with an integer programming model.

A flexible variable neighbourhood search algorithm for different variants of the Electric Vehicle Routing Problem

Block approach—tabu search algorithm for single machine total weighted tardiness problem

Genetic algorithm for the one-commodity pickup-and-delivery traveling salesman problem

Node placement problems have been long investigated in the optimization field due to numerous applications in facility location, logistics, services, etc. Such problems are attracting again the attention of researchers now from the networking domain, and more especially from Wireless Mesh Networks (WMNs) field. Indeed, the placement of mesh routers nodes appears to be crucial for the performance and operability of WMNs, in terms of network connectivity and stability. However, node placement problems are known for their hardness in solving them to optimality, and therefore heuristics methods are approached to near-optimally solve such problems. In this work we evaluate the performance of different heuristic methods in order to judge on their suitability of solving mesh router nodes problem. We have selected methods from two different families, namely, local search methods (Hill Climbing and Simulated Annealing) and population-based methods (Genetic Algorithms). The former are known for their capability to exploit the solution space by constructing a path of visited solutions, while the later methods use a population of individuals aiming to largely explore the solution space. In both cases, a bi-objective optimization consisting in the maximization of the size of the giant component in the mesh routers network (for measuring network connectivity) and that of user coverage are considered. In the experimental evaluation, we have used a benchmark of instances--varying from small to large size--generated using different distributions of mesh node clients (Uniform, Normal, Exponential and Weibull).