Optimizing the geodetic networks based on the distances between the net points and the project border

Geodetic networks are important for most engineering projects. Generally, a geodetic network is designed according to precision, reliability, and cost criteria. This paper provides a new criterion considering the distances between the Net Points (NPs) and the Project Border (PB) in terms of Neighboring (N). Optimization based on the N criterion seeks to relocate the NPs as close as possible to PB, which leads to creating shorter distances between NPs or those distances linking NPs with Target Points (TPs) to be measured inside PB. These short distances can improve the precision of NPs and increase the accuracy of observations and transportation costs between NPs themselves or between NPs and TPs (in real applications). Three normalized N objective functions based on L1, L2, and L∞‒norms were formulated to build the corresponding N optimization models, NL1; NL2; and NL∞ and to determine the best solution. Each model is subjected to safety, precision, reliability, and cost constraints. The feasibility of the N criterion is demonstrated by a simulated example. The results showed the ability of NL∞ to determine the safest positions for the NPs near PB. These new positions led to improving the precision of the network and preserving the initial reliability and observations cost, due to contradiction problems. Also, N results created by all N models demonstrate their theoretical feasibility in improving the accuracy of the observations and transportation cost between points. It is recommended to use multi-objective optimization models to overcome the contradiction problem and consider the real application to generalize the benefits of N models in designing the networks.

A study of uncertain multi-objective nonlinear programming problems for rough intervals

In real conditions, the parameters of multi-objective nonlinear programming (MONLP) problem models can’t be determined exactly. Hence in this paper, we concerned with studying the uncertainty of MONLP problems. We propose algorithms to solve rough and fully-rough-interval multi-objective nonlinear programming (RIMONLP and FRIMONLP) problems, to determine optimal rough solutions value and rough decision variables, where all coefficients and decision variables in the objective functions and constraints are rough intervals (RIs). For the RIMONLP and FRIMONLP problems solving methodology are presented using the weighting method and slice-sum method with Kuhn-Tucker conditions, We will structure two nonlinear programming (NLP) problems. In the first one of this NLP problem, all of its variables and coefficients are the lower approximation (LAI) it’s RIs. The second NLP problems are upper approximation intervals (UAI) of RIs. Subsequently, both NLP problems are sliced into two crisp nonlinear problems. NLP is utilized because numerous real systems are inherently nonlinear. Also, rough intervals are so important for dealing with uncertainty and inaccurate data in decision-making (DM) problems. The suggested algorithms enable us to the optimal solutions in the largest range of possible solution. Finally, Illustrative examples of the results are given.

Scheduling in parallel machines with two objectives: analysis of factors that influence the Pareto frontier

This paper explores the characteristics of solutions when scheduling jobs in a shop with parallel machines. Three classical objective functions were considered: makespan, total completion time, and total tardiness. These three criteria were combined in pairs, resulting in three bi-objective formulations. These formulations were solved using the ε-constraint method to obtain a Pareto frontier for each pair. The objective of the research is to evaluate the Pareto set of efficient schedules to characterize the solution sets. The characterization of the solutions sets is based on two performance metrics: the span of the objective functions' values for the points in the frontier and their closeness to the ideal point. Results that consider four experimental factors indicate that when the makespan is one of the objective functions, the range of the processing times among jobs has a significant influence on the characteristics of the Pareto frontier. Simultaneously, the slack of due dates is the most relevant factor when total tardiness is considered.

A Comparative Study of the Application of Different Commercial Software for Topology Optimization

Topology optimization (TO) has been a popular design method among CAD designers in the last decades. This method optimizes the given design domain by minimizing/maximizing one or more objective functions, such as the structure’s stiffness, and at the same time, respecting the given constraints like the volume or the weight reduction. For this reason, the companies providing the commercial CAD/FEM platforms have taken this design trend into account and, thus, have included TO in their products over the last years. However, it is not clear which features, algorithms, or, in other words, possibilities the CAD designers do have using these software platforms. A comparative study among the most applied topology optimization software was conducted for this research paper. First, the authors developed an online database of the identified TO software in the form of a table. Interested CAD designers can access and edit its content, contributing in this way to the creation of an updated library of the available TO software. In addition, a deeper comparison among three commercial software platforms—SolidWorks, ANSYS Mechanical, and ABAQUS—was implemented using three common case studies—(1) a bell crank lever, (2) a pillow bracket, and (3) a small bridge. These models were designed, optimized, and validated numerically, as well as compared for their strength. Finally, the above software was evaluated with respect to optimization time, optimized designs, and TO possibilities and features.

Different Meta-Heuristic Optimization Techniques and Their Application in Solar Photovoltaic Field

A method of optimization is used to resolve issues smartly by selecting the better option from various existing possibilities. Many optimization problems are possessing characteristics, namely nonlinearity, complexity, multimodal approach, and incompatible objective functions. Sometimes even for individual simple and linear type objective functions, a solution that is optimal and does not exist, there is uncertainness of obtaining the best solution. The aim of finding methods that can resolve various issues in a defined manner potentially has found the concentration of different researchers responsible for performing the advancement of a new “intelligent” technique called meta-heuristics technique. In the last few years, there is an advancement of various meta-heuristics techniques in different areas or various fields. Meta-heuristics are a demanded thrust stream of research that showed important advancement in finding the answer to problems that are optimized. The chapter gives the guidance for enhancing research more meaningfully.

A Novel Fuzzy logic based Improved Cuckoo Search Algorithm

Cuckoo Search (CS) algorithm is a nature-inspired optimization algorithm (NIOA) with less control parameters that is stable, versatile, and easy to implement. CS has good global search capabilities, but it is prone to local optima problems. As a result, it may be possible to improve the classic CS algorithm's optimization capability. Centered on fuzzy set theory, this paper introduces an improved CS version. The population of solutions has been divided into two fuzzy sets, and each solution is assigned to one of the sets based on its fitness. The fuzzy collection centroids, global best solution advice, and Lévy distribution dependent mutation are all used to boost the population's solutions. With well-accepted objective functions such as Otsu inter class variance and Kapur's entropy, the experimental analysis has been conducted on the CEC-2014 test suite and image multi-level thresholding domain. The proposed fuzzy cuckoo search (FCS) algorithm is compared to the classical CS, PSO, FA, SMA, and BA algorithm and provides satisfactory results.

Optimizing the effect of charging electric vehicles on distribution transformer using demand side management

This paper mainly aims to present the demand side management (DSM) of electric vehicles (EVs) by considering distribution transformer capacity at a residential area. The objective functions are formulated to obtain charging schedule for individual owner by i) minimizing the load variance and ii) price indicated charging mechanism. Both the objective functions profit the owner in the following ways: i) fulfilling their needs, ii) minimizing overall charging cost, iii) lessening the peak load, and iv) avoiding the overloading of distribution transformer. The proposed objective functions were worked on a residential area with 8 houses each house with an EV connected to a 20 kVA distribution transformer. The formulations were tested in LINGO platform optimization modeling software for linear, nonlinear, and integer programming. The results obtained were compared which gives good insight of EV load scheduling without actual price prediction.

A note on the complexity of the bilevel bottleneck assignment problem

We establish the NP-completeness of the variant of the bilevel assignment problem, where the leader and the follower both have bottleneck objective functions and were the follower behaves according to the optimistic rule. This result settles a problem that has been left open by Klinz & Gassner [4OR 7:379–394, 2009].