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The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and genetic hybrid algorithm (PGSA-GA), for solving structural optimization problems.

PGSA-GA is based on PGSA and three improved strategies, namely, elitist strategy of morphactin concentration calculation, strategy of intelligent variable step size and strategy of initial growth point selection based on GA. After a detailed formulation and explanation of its implementation, PGSA-GA is verified using the examples of typical truss and single-layer lattice shell.

Improved PGSA-GA was implemented and optimization was carried out for two typical optimization problems; then, a comparison was made between the PGSA-GA and other methods. The results show that the method proposed in the paper has the advantages of high efficiency and rapid convergence, which enable it to be used for the optimization of various types of steel structures.

Through the examples of typical truss and single-layer lattice shell, it shows that the optimization efficiency and effect of PGSA-GA are better than those of other algorithms and methods, such as GA, secondary optimization method, etc. The results show that PGSA-GA is quite suitable for structural optimization.

The term selection problem for selecting query terms in information filtering and routing has been investigated using hill‐climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches, which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrieval effectiveness of filtering queries in these TREC experiments. Hill‐climbers are, however, likely to get trapped in local optima, and the use of more sophisticated local search techniques for this problem that attempt to break out of these optima are worth investigating. To this end, this paper aims to apply a genetic algorithm (GA) to the same problem.

A standard TREC test collection is used from the TREC‐8 filtering track, recording mean average precision and recall measures to allow comparison between the hill‐climber and GAs. It also varies elements of the GA, such as probability of a word being included, probability of mutation and population size in order to measure the effect of these variables. Different strategies such as elitist and non‐elitist methods are used, as well as roulette wheel and rank selection GAs.

The results of tests suggest that both techniques are, on average, better than the baseline, but, the implemented GA does not match the overall performance of a hill‐climber. The Rank selection algorithm does better on average than the Roulette Wheel algorithm. There is no evidence in this study that varying word inclusion probability, mutation probability or Elitist method make much difference to the overall results. Small population sizes do not appear to be as effective as larger population sizes.

The evidence provided here would suggest that being stuck in a local optima for the term selection optimization problem does not appear to be detrimental to the overall success of the hill‐climber. The evidence from term rank order would appear to provide extra useful evidence, which hill climbers can use efficiently, and effectively, to narrow the search space.

The paper represents the first attempt to compare hill‐climbers with GAs on a problem of this type.

The purpose of this paper is to design an experimentally‐oriented program for the training of a new generation of educational a administrators. The rationale for the program is based on selected concepts and propositions of occupational sociology, organization theory, and systems theory. Some of the salient features of the program are as follows: (i) The design is guided by the logic of Campbell's quasi‐experiment. (ii) A principal goal is to stimulate the professionalization of educational administration by increasing (a) the body of systematic knowledge: and (b) the commitment to an ideal of service in education. (iii) Another major goal is to sensitize educational administrators to the dilemmas of organizational change and to strategies for inducing change. (iv) A systems analysis is set forth of five sequentially interrelated processes: goal formation, recruitment of faculty and students, specification of the content of the curriculum, placement of graduates, and an evaluation of the program. (v) A sample curriculum for a three‐year period, guided by six pedagogical conceptions. (vi) A design for an experimental program for four cohorts of students is outlined.

Waste production and related environmental problems have caused urban services management many problems in collecting, transporting and disposal of waste. The purpose of this study is to design a new model for municipal waste collection vehicle routing problems with time windows and energy generating from waste. To this purpose, a bi-objective model is presented with the objectives of increasing the income of waste recycles and energy generation from waste and reducing emissions from environmental pollutants.

A bi-objective model is presented with the objectives of increasing income of recycles trade and energy generation and reducing emissions from environmental pollutants. Concerning the complexity of the model and its inability to solve large-scale problems, non-dominated sorting genetic algorithms and multi-objective particle swarm optimization algorithms are applied.

In this research, an integrated approach to urban waste collection modeling that coordinates the various activities of waste management in the city of Kermanshah and energy generation from waste are provided. Besides, this study calculates the criteria that show the environmental effects of municipal waste. The proposed model helps to collect municipal wastes in the shortest possible time in addition to reducing the total cost, revenues from the sale of recycled materials and energy production.

The proposed model boosts the current understanding of the waste management and energy generation of waste. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

This paper seeks to improve solution procedures for solving a larger version of the integrated preventive maintenance planning and production scheduling model with a total weighted expected tardiness objective function introduced in a 2003 paper by Cassady and Kutanoglu using a genetic algorithm heuristic procedure.

In this paper, heuristics based on genetic algorithms are developed to solve the integrated model.

The performance of the proposed genetic algorithm heuristics are evaluated using multiple instances of several problem sizes. The results indicate that the proposed genetic algorithms can effectively be used to solve the integrated problem.

The heuristics presented in this paper significantly improve the ability of the decision‐maker to consider larger instances of the integrated model. One may ask, “how significant is that improvement?” The answer depends on the specific industrial context under consideration and the definition of a “job”.

Typically, production scheduling and preventive maintenance planning is planned and executed independently in spite of the inter‐dependent relationship between them. However, the 2003 paper by Cassady and Kutanoglu demonstrates the benefit of using the integrated model to solve these two problems simultaneously. However, their solution procedure is limited to small problems (6‐jobs or less). Therefore, this study intends to improve the solution procedure to solve larger instances of the problem.

The study aims to highlight and understand, and bring the human agency into the debate on the theory of normative control. While, the previous literature has highlighted the problem of the missing subject. However, the actual human agency in terms of agential properties has not been seriously addressed. This study is an attempt to overcome this problem of the missing subject.

A two-phase design inspired by retroductive inference was adopted for this study. In the first phase, abduction was used to explore the literature on normative control to highlight the forces of attraction, which may pull the employees to participate willingly within normative control systems. In the second phase, following retroductive inference, agential explanations of the forces of attraction identified in the first phase were explored by venturing into other related fields, e.g. psychology and sociology.

The study highlights four strategies used by organizations using normative control, i.e. comfort zoning, relational bonding, moral trapping and elitist appeal. These strategies rely on attractive forces. These forces of attraction pull employees to participate in the normative control system. The attractive element in the identified strategies is due to the fact that these strategies target specific agential properties, i.e. the need for comfort, sense of belonging, moral agency and pride. Overall, the findings suggest that individuals drive their concerns from culture but in relation to their capacity as needy beings for being enculturated.

Theoretically, this study adds conceptual strength to the explanations of normative control. It is suggested that neglect of human agency renders explanations conceptually weak. The study fills this gap in the research. Practically, this study would be beneficial for better design and implementation of normative control. Several studies have pointed out that normative control does not yield the intended results. Out of many reasons, a lack of understanding of human agency is a major cause of unsuccessful attempts to normatively control employees. This study provides some basis to understand the human subject for better design of soft systems of control.

To the best of the authors’ knowledge, this is the first research study that explores agential properties with reference to normative control systems. This study is important for researchers and practitioners.

A new design framework for crossover operator is proposed based on the commonality concept. In the reproduction process the resulting hybrid crossover operator includes a local search scheme aiming to improve the genetic characteristics of the offspring. Commonality suggests that search should be driven in the neighbourhood of parents, and local optimisers can drive this search. The ranking of the offspring candidates is based on a local fitness function using approximations and appropriated heuristics linked to the structural optimisation problem. The goal of this approach is to identify and preserve the common schema of the two parents responsible for their high‐observed fitness. The proposed hybrid crossover operator is embedded into a genetic algorithm supported by an elitist strategy and its performance is compared with the parametrised uniform crossover.

A numerical method for shape optimisation in forging is presented. The goal of the optimisation is to eliminate work‐piece defects that may arise during the forging process. A two‐dimensional finite element code has been developed for the simulation of the mechanical process. The material is incompressible and it follows the Norton‐Hoff law. To deal with contact constraint the velocity projection algorithm is used. The optimisation process is conducted using a genetic algorithm supported by an elitist strategy. A new genetic operator called adaptive mutation has been developed to increase the efficiency of the search. The developed scheme is used to design optimal preform shapes for several axisymmetric examples. Continuous and discrete design variables are considered. The objective function of the optimisation problem is associated with the quality of the final product. Comparing the obtained optimal results with the literature validates the proposed optimisation method.

The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA).

The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures.

The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution.

The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality.

The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs.

The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.

In evolutionary robotics (ER), robotic control systems are subject to a developmental process inspired by natural evolution. The purpose of this paper is to utilize a control system representation based on finite state machines (FSMs) to build a decentralized online‐evolutionary framework for swarms of mobile robots.

A new recombination operator for multi‐parental generation of offspring is presented and a known mutation operator is extended to harden parts of genotypes involved in good behavior, thus narrowing down the dimensions of the search space. A storage called memory genome for archiving the best genomes of every robot introduces a decentralized elitist strategy. These operators are studied in a factorial set of experiments by evolving two different benchmark behaviors such as collision avoidance and gate passing on a simulated swarm of robots. A comparison with a related approach is provided.

The framework is capable of robustly evolving the benchmark behaviors. The memory genome and the number of parents for reproduction highly influence the quality of the results; the recombination operator leads to an improvement in certain parameter combinations only.

Future studies should focus on further improving mutation and recombination. Generality statements should be made by studying more behaviors and there is a need for experimental studies with real robots.

The design of decentralized ER frameworks is improved.

The framework is robust and has the advantage that the resulting controllers are easier to analyze than in approaches based on artificial neural networks. The findings suggest improvements in the general design of decentralized ER frameworks.