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Most real world engineering design problems, such as cross-country water mains, include combinations of continuous, discrete, and binary value decision variables. Very often, the binary decision variables associate with the presence and/or absence of some nominated alternatives or project’s components. This study extends an existing continuous Ant Colony Optimization (ACO) algorithm to simultaneously handle mixed-variable problems. The approach provides simultaneous solution to a binary value problem with both discrete and continuous variables to locate and size design components of the proposed system. This paper shows how the existing continuous ACO algorithm may be revised to cope with mixed-variable search spaces with binary variables. Performance of the proposed version of the ACO is tested on a set of mathematical benchmark problems followed by a highly nonlinear forced water main optimization problem. Comparing with few other optimization algorithms, the proposed optimization method demonstrates satisfactory performance in locating good near optimal solutions.

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A constrained version of the Big Bang-Big Crunch algorithm for the efficient solution of the optimal reservoir operation problems is proposed in this paper. Big Bang-Big Crunch (BB-BC) algorithm is a new meta-heuristic population-based algorithm that relies on one of the theories of the evolution of universe namely, the Big Bang and Big Crunch theory. An improved formulation of the algorithm named Constrained Big Bang-Big Crunch (CBB-BC) is proposed here and used to solve the problems of reservoir operation. In the CBB-BC algorithm, all the problems constraints are explicitly satisfied during the solution construction leading to an algorithm exploring only the feasible region of the original search space. The proposed algorithm is used to optimally solve the water supply and hydro-power operation of “Dez” reservoir in Iran over three different operation periods and the results are presented and compared with those obtained by the basic algorithm referred to here as Unconstrained Big Bang–Big Crunch (UBB–BC) algorithm and other optimization algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) and those obtained by Non-Linear Programming (NLP) technique. The results demonstrate the efficiency and robustness of the proposed method to solve reservoir operation problems compared to alternative algorithms.

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This paper is concerned with the economical comparison between two commonly used configurations for double layer grids and determining their optimum span-depth ratio. Two ranges of spans as small and big sizes with certain bays of equal length in two directions and various types of element grouping are considered for each type of square grids. In order to carry out a precise comparison between different systems, optimum design procedure based on the Cuckoo Search (CS) algorithm is developed. The CS is a meta-heuristic algorithm recently developed that is inspired by the behavior of some Cuckoo species in combination with the Lévy flight behavior of some birds and insects. The design algorithm obtains minimum weight grid through appropriate selection of tube sections available in AISC Load and Resistance Factor Design (LRFD). Strength constraints of AISC-LRFD specification and displacement constraints are imposed on grids. The comparison is aimed at finding the depth at which each of the different configurations shows its advantages. The results are graphically presented from which the optimum depth can easily be estimated for each type, while the influence of element grouping can also be realized at the same time.

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In this paper, an efficient multi-objective model is proposed to solve time-cost trade off problem considering cash flows. The proposed multi-objective meta-heuristic is based on Ant colony optimization and is called Non Dominated Archiving Ant Colony Optimization (NAACO). The significant feature of this work is consideration of uncertainties in time, cost and more importantly interest rate. A fuzzy approach is adopted to account for uncertainties. Mathematics of cash-flow analysis in a fuzzy environment is described. A case study is done using the proposed approach

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Ant colony optimization algorithms (ACOs) have been basically introduced to discrete variable problems and applied to different research domains in several engineering fields. Meanwhile, abundant studies have been already involved to adapt different ant models to continuous search spaces. Assessments indicate competitive performance of ACOs on discrete or continuous domains. Therefore, as potent optimization algorithms, it is encouraging to involve ant models to mixed-variable domains which simultaneously tackle discrete and continuous variables. This paper introduces four ant-based methods to solve mixed-variable problems. Each method is based upon superlative ant algorithms in discrete and/or continuous domains. Proposed methods’ performances are then tested on a set of three mathematical functions and also a water main design problem in engineering field, which are elaborately subject to linear and non-linear constraints. All proposed methods perform rather satisfactorily on considered problems and it is suggested to further extend the application of methods to other engineering studies.

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Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand time-cost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall safety risk (OSR). The present GA-based optimization model possesses significant features of Pareto ranking as selection criterion, elite archiving and adaptive mutation rate. In order to facilitate safety risk assessment in the planning phase, a qualitative activity-based safety risk (QASR) method is also developed. An automated system is codded as an Excel add-in program to facilitate the use of the model for practitioners and researchers. The model has been implemented and verified on a case study successfully. Results indicate that integration of safety risk assessment methods into multi-objective TCO problem improves OSR of nondominated solutions. The robustness of the present optimization model has also been proved by its great ability to prevent genetic drift as well as the improvement in the bicriteria among generations.

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Location and types of sensors may be integrated for simultaneous achievement of water security goals and other water utility objectives, such as regulatory monitoring requirements. Complying with the recent recommendations on dual benefits of sensors, this study addresses the optimal location of these types of sensors in a multipurpose approach. The study presents two mathematical models for optimum location of sensors as static double use benefit model (SDUBM) and dynamic double use benefit model (DDUBM) which provides tradeoffs between maximum monitored volume of water known as “demand coverage” and minimum consumption of contaminated water. In the proposed modeling scheme, sensors are located to maximize dual use benefits of achieving water security goals and accomplishing regulatory monitoring requirements. The validity of the model is tested using two extensively tested example problems with multi-objective ant colony optimization (ACO) algorithm. The Pareto front for different number of sensors are presented and discussed.

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In this paper, the effect of angle between predictor and corrector surfaces on the structural analysis is investigated. Two objective functions are formulated based on this angle and also the load factor. Optimizing these functions, and using the structural equilibrium path’s geometry, lead to two new constraints for the nonlinear solver. Besides, one more formula is achieved, which was previously found by other researchers, via a different mathematical process. Several benchmark structures, which have geometric nonlinear behavior, are analyzed with the proposed methods. The finite element method is utilized to analyze these problems. The abilities of suggested schemes are evaluated in tracing the complex equilibrium paths. Moreover, comparison study for the required number of increments and iterations is performed. Results reflect the robustness of the authors’ formulations.

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Parallel Cellular Automata (PCA) previously has been employed for optimizing bi-objective reservoir operation, where one release is used to meet both objectives. However, if a single release can only be used for one objective, meaning two separate sets of releases are needed, the method is not applicable anymore. In this paper, Multi-Step Parallel Cellular Automata (MSPCA) has been developed for bi-objective optimization of single-reservoir systems’ operation. To this end, a novel cellular automata formulation is proposed for such problems so that PCA’s incapability when dealing with them will be overcome. In order to determine all releases throughout the operation period, in each iteration – unlike PCA – two updates take place so as to calculate releases individually. Since a bi-objective problem in Dez reservoir (in southern Iran) has been solved by PCA in earlier works, the same data is used here. The results are given for a 60-months operation period, and to evaluate this method, the results of Non-Dominated Sorting Genetic Algorithm (NSGAII) is also given for the same problem. The comparison shows MSPCA, beside remarkable reduction in computational costs, gives up solutions with higher quality as well.