Nondominated Research Articles

Multiobjective optimal power flow solutions using nondominated sorting colliding bodies optimization

In the current work, an innovative nondominated sorting colliding bodies optimization (NSCBO) technique is introduced to tackle multiobjective optimal power flow (MOOPF) challenges within electrical power networks. This method offers a means to generate a diverse array of nondominated solutions in a single iteration by including the nondominated (ND) sorting process and the concept of crowding distance. Additionally, it utilizes a spread indicator to archive the latest nondominated solutions. In the NSCBO method, the mass of each colliding body is determined by its nondominated rank rather than relying on objective function information. Moreover, a fuzzy decision-making strategy is employed to identify a suitable solution from the set of ND solutions. To showcase the scalability and viability of the NSCBO method, experiments are conducted on IEEE 30-bus, considering both bi- and tri-objective models. Comparative analysis with existing methods from recent literature demonstrates the efficacy of the NSCBO technique in handling constraints and deriving nondominated solutions for MOOPF problems.

Multi-Objective Optimal Control of Autocatalytic Esterification Process Using Control Vector Parameterization (CVP) and Hybrid Strategy (HS)

The semi-batch esterification of propionic anhydride (PA) with 2-butanol (BT) in the presence of catalyst can be optimised using an optimal control strategy, which utilises the reactor temperature (TR) and feed (FR) flowrate. However, the opposing objective functions, which are maximum conversion (XM) and minimum process time (tf) in the autocatalytic esterification process, could complicate the optimisation strategy. Simultaneous optimisation of various objectives results in a multi-objective optimal control (MOOC) problem with numerous solutions known as non-dominated (ND) points. In this paper, control vector parameterisation (CVP) and hybrid strategy (HS) are utilised to form Pareto Front (PF) for two opposite targets, which are first to increase XM and secondly to reduce tf. Each ND point comprises variant optimal dynamic tracks of TR and FR, which results in various targets of XM and tf. These solutions provide numerous options for evaluating trade-offs and deciding on the most efficient operating strategy. It is found that the ND point in zone II can be selected as the trade-off of the optimal TR and FR in this study.

A modified Marine predators algorithm for solving single- and multi-objective combined economic emission dispatch problems

Decreasing the emission of greenhouse gases and the fuel cost are the main principles of the combined economic emission dispatch (CEED) optimization problem. In this work, a modified version of the Marine Predators Algorithm (MMPA), is proposed and applied for solving single- and bi-objective CEED problems. MMPA is suggested to improve the performance of conventional MPA. It includes a comprehensive learning approach to share the best experiences among all the individuals to avoid premature convergence. The MMPA increases the populations' effectiveness to reach the optimum fitness solution and this is helpful in cases of single- and bi-objective functions. The proposed MMPA has been mathematically assessed with twenty-eight 50-dimensional benchmarks of CEC2017. Also, it is successfully tested on four electrical power generation systems (each of them with 3, 5, 6, and 26 generating units respectively). The results achieved by MMPA are compared with those obtained by the original MPA and other recent optimization methods. Concerning the bi-objective problem, the Pareto approach is combined with the proposed MMPA and conventional MPA techniques to reach a group of non-dominated (ND) solutions, and then the fuzzy method is utilized to choose the best compromise solution. The reached results display that the proposed MMPA technique gives better performance than the conventional MPA and the others.

Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch

In this paper, Chaotic Artificial Ecosystem-based Optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves the economical operation of the electrical power system and reducing the environmental pollution produced by the conventional power generation. Here, the Combined Economic Emission Dispatch (CEED) problem is represented using a max/max Price Penalty Factor (PPF) to confine the system's nonlinearity. PPF is considered to transform a four-objective problem into a single-objective optimization problem. The proposed modification of AEO raises the effectiveness of the populations to achieve the best fitness solution by well-known 10 chaotic functions and this is valuable in both cases of the single and multi-objective functions. The CAEO algorithm is used for minimizing the economic load dispatch and the three bad gas emissions which are sulfur dioxide (SO2), nitrous oxide (NOx), and carbon dioxide (CO2). To evaluate the proposed CAEO, it is utilized for four different levels of demand in a 6-unit power generation (30-bus test system) and 11-unit power generation (69-bus test system) with a different value of load demand (1000, 1500, 2000, and 2500MW). Statistical analysis is executed to estimate the reliability and stability of the proposed CAEO method. The results obtained by CAEO algorithm are compared with other methods and conventional AEO to prove that the modification is to boost the search strength of conventional AEO. The results display that the CAEO algorithm is superior to the conventional AEO and the others in achieving the best solution to the problem of CEED in terms of efficient results, strength, and computational capability all over study cases. In the second scenario of the bi-objective problem, the Pareto theory is integrated with a CAEO to get a series of Non-Dominated (ND) solutions, and then using the fuzzy approach to determine BCS.

SASS: slicing with adaptive steps search method for finding the non-dominated points of tri-objective mixed-integer linear programming problems

Multi-objective optimization problems (MOOP) reflect the complexity of many real-world decision problems where objectives are conflicting. The presence of more than one criterion makes finding the non-dominated (ND) points a crucial issue in the decision making process. Tri-objective mixed-integer linear programs (TOMILP) are an important subclass of MOOPs that are applicable to many problems in economics, business, science, and engineering including sustainable systems that must consider economic, environmental, and social concerns simultaneously. The literature on finding the ND points of TOMILPs is limited; there are only a few algorithms published in the literature that do not guarantee generating the entire ND points of TOMILPs. We present a new method, the Slicing with Adaptive Steps Search (SASS), to generate the ND points of TOMILPs. The result of SASS is primarily a superset of the set of ND points in the form of (partially) ND faces. We then perform a post-processing to eliminate the dominated parts of the partially ND faces. We provide a theoretical analysis of SASS and illustrate its effectiveness on a large set of instances.

A multi-criteria decision analysis to include environmental, social, and cultural issues in the sustainable aggregate production plans

Aggregate production planning (APP) that is an important concept of supply chain management (SCM), is one of the tools to determine production rates, inventory levels, and workforce requirements for fulfilling customer demands in a multi-period setting. Traditional APP models employ a single objective function to optimize monetary issues only. In this paper, we present a multi-objective APP model to analyze economic, social, environmental, and cultural pillars inclusively; moreover, each pillar includes several sub-pillars in the model. The resulting model includes an accurate representation of the problem with binary and continuous variables under sustainability considerations. We illustrate the effectiveness of the model in an appliance manufacturer and solve the problem using an exact solution method for multi-objective mixed-integer linear programs (MOMILP). We find a large number of the non-dominated (ND) points in the objective function space and analyze their trade-offs systematically. We show how this framework supports multiple criteria decision making process in the APP problems in the presence of sustainability considerations. Our approach provides a comprehensive analysis of the ND points of sustainable APP (SAPP) problems, and hence, the trade-offs of objective functions are insightful to the decision makers.

GoNDEF: an exact method to generate all non-dominated points of multi-objective mixed-integer linear programs

Most real-world problems involve multiple conflicting criteria. These problems are called multi-criteria/multi-objective optimization problems (MOOP). The main task in solving MOOPs is to find the non-dominated (ND) points in the objective space or efficient solutions in the decision space. A ND point is a point in the objective space with objective function values that cannot be improved without worsening another objective function. In this paper, we present a new method that generates the set of ND points for a multi-objective mixed-integer linear program (MOMILP). The Generator of ND and Efficient Frontier (GoNDEF) for MOMILPs finds that the ND points represented as points, line segments, and facets consist of every type of ND point. First, the GoNDEF sets integer variables to the values that result in ND points. Fixing integer variables to specific values results in a multi-objective linear program (MOLP). This MOLP has its own set of ND points. A subset of this set establishes a subset of the ND points set of the MOMILP. In this paper, we present an extensive theoretical analysis of the GoNDEF and illustrate its effectiveness on a set of instance problems.

Comparison of Calcium Dynamics and Specific Features for G Protein–Coupled Receptor–Targeting Drugs Using Live Cell Imaging and Automated Analysis

G protein–coupled receptors (GPCRs) are targets for designing a large fraction of the drugs in the pharmaceutical industry. For GPCR-targeting drug screening using cell-based assays, measurement of cytosolic calcium has been widely used to obtain dose–response profiles. However, it remains challenging to obtain drug-specific features due to cell-to-cell heterogeneity in drug–cell responses obtained from live cell imaging. Here, we present a framework combining live cell imaging of a cell population and a feature extraction method for classification of responses of drugs targeting GPCRs CXCR4 and α2AR. We measured the calcium dynamics using confocal microscopy and compared the responses for SDF-1α and norepinephrine. The results clearly show that the clustering patterns of responses for the two GPCRs are significantly different. Additionally, we show that different drugs targeting the same GPCR induce different calcium response signatures. We also implemented principal component analysis and k means for feature extraction and used nondominated (ND) sorting for ranking a group of drugs at various doses. The presented approach can be used to model a cell population as a mixture of subpopulations. It also offers specific advantages, such as higher spatial resolution, classification of responses, and ranking of drugs, potentially providing a platform for high-content drug screening.

Binary decision diagrams for generating and storing non-dominated project portfolios with interval-valued project scores

When selecting a portfolio (i.e., set of) projects, the projects are often evaluated by additive scores with respect to multiple attributes. Uncertainty or incomplete information about projects’ scores can be modeled with plausible lower and upper bounds on the projects’ scores. It is recommended to select a non-dominated (ND) portfolio, that is a portfolio such that it is not possible to select another portfolio which has (i) at least as high value with respect to every attribute for all plausible scores, (ii) and has strictly higher value with respect to at least one attribute for some plausible scores. In this paper, we lay a foundation on computing (ND) project portfolios. We also present an algorithm based on binary decision diagrams (BDDs) for generating the ND portfolios. We show that our algorithms can provide significant computational advantages over previous algorithms. We also explore how BDDs can be used for storing large numbers of ND portfolios and how such BDDs can be efficiently generated.

Nondominated local coteries for resource allocation in grids and clouds

The resource allocation problem is a fundamental problem in Grid and Cloud computing environments. This paper focuses on constructing nondominated ( ND) local coteries to solve the problem in a distributed way. Distributed algorithms using coteries usually incur low communication overheads and have high degrees of fault-tolerance, and ND coteries are candidates for the algorithms to achieve the highest degree of fault-tolerance. A new type of coteries, called p-coteries, is defined to aid the construction of local coteries. Theorems about the nondomination of p-coteries are then developed, and an operation, called pairwise-union ( p-union), is proposed to help generate ND p-coteries, which in turn can be used to generate ND local coteries for solving the resource allocation problem.

Comments on "On the Nondomination of Cohorts Coteries"

For original paper see Jiang, ibid., vol.53, p.922-923 (2004). Jiang shows a result that a Cohorts coterie is nondominated (ND) under a Cohorts structure Coh(k,n) = (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ,..., C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) if |C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> | = k. However, the result is incorrect. In this comment, we correct the result by deriving the necessary and sufficient conditions for the Cohorts coterie to be ND. As we will see, a Cohorts coterie under Coh(k,n) is ND if and only if k = 1 or n = 1.

Transversal merge operation: a nondominated coterie construction method for distributed mutual exclusion

A coterie is a set of subsets (called quorums) of the processes in a distributed system such that any two quorums intersect with each other and is mainly used to solve the mutual exclusion problem in a quorum-based algorithm. The choice of a coterie sensitively affects the performance of the algorithm and it is known that nondominated (ND) coteries achieve good performance in terms of criteria such as availability and load. On the other hand, grid coteries have some other attractive features: 1) a quorum size is small, which implies a low message complexity, and 2) a quorum is constructible on the fly, which benefits a low space complexity. However, they are not ND coteries unfortunately. To construct ND coteries having the favorite features of grid coteries, we introduce the transversal merge operation that transforms a dominated coterie into an ND coterie and apply it to grid coteries. We call the constructed ND coteries ND grid coteries. These ND grid coteries have availability higher than the original ones, inheriting the above desirable features from them. To demonstrate this fact, we then investigate their quorum size, load, and availability, and propose a dynamic quorum construction algorithm for an ND grid coterie.

On the nondomination of cohorts coteries

We show that subsets of Cohorts coteries proposed in [J.-R. Jiang et al., (1997)] are nondominated (ND) k-coteries, which are candidates to achieve the highest availability when utilized to solve the distributed k-mutual exclusion problem and the distributed h-out of-k mutual exclusion problem.

Risk Metric for Multi‐Objective Design of Naval Ships

ABSTRACTThe DoD Risk Management Guide requires risk assessment of acquisition performance, cost, and schedule through the identification, subsequent analysis and prioritization of adverse program events based on their probability and consequences. This type of risk assessment is very important in concept exploration and design when considering new technologies, unique processes, and novel concepts.Uncertainty associated with the design process itself and the definition and selection of specific design alternatives can also have a significant impact on performance, cost, and schedule risk. Inherent, statistical and modeling uncertainty and uncertainty because of human error, must be considered in the design process, but uncertainty analysis requires a more detailed and computationally intensive probabilistic approach. It is most appropriate for post‐exploration design optimization, after specific cost and performance goals and thresholds have been set to maximize the probability of achieving these goals.In this paper, a two‐stage concept design strategy is proposed that uses a multi‐objective optimization and simplified risk event approach for concept exploration and a more rigorous multi‐disciplinary optimization with uncertainty for concept development. Concept exploration identifies nondominated design concepts and establishes the optimum relationship between effectiveness, cost, and risk given a broad selection of technologies and design alternatives. In this context, non‐dominated (N‐D) refers to designs with the maximum effectiveness for a given cost and level of risk. This is a global optimization design problem that considers a wide range of performance, cost and risk possibilities. Risk is defined using a separate objective attribute, an overall measure of risk (OMOR), which specifically addresses the high‐risk events associated with the selection of new technologies, processes, and concepts. With this perspective, decision‐makers may establish ratio‐nal requirements, select technologies, narrow the design space, and establish a non‐dominated concept baseline design or set of designs.Once these early decisions are made, concept development and the remaining design phases add detail, refine requirements and reduce risk. Optimization continues into concept development, but a single objective optimization based on uncertainty analysis is used, maximizing the probability of success (POS) of satisfying cost and effectiveness thresholds and other constraints established in concept exploration.The methodology and a simple application of the multi‐objective optimization and risk event approach are described in this paper.

A coterie‐based mutual exclusion algorithm for distributed systems allowing multiple process failures at arbitrary time

AbstractIn this article, we propose a coterie‐based mutual exclusion algorithm for distributed systems allowing process failures at arbitrary time. The proposed algorithm is an extension of Maekawa's algorithm, which is a representative conventional method for distributed mutual exclusion based on coterie. The proposed algorithm realizes mutual exclusion by detecting process failures using timers, updating the coterie dynamically, and introducing the system to a state without failure. It has the following advantages. First, it inherits all advantages of Maekawa's algorithm, because it behaves in the same way as Maekawa's algorithm when there is no process failure. Also, it allows k(&lt;N; N is the number of processes) process failures at arbitrary time, so the assumption on failure is close to real systems. Furthermore, it is assured that the updated coterie is an ND (nondominated) coterie if the initial coterie is an ND coterie. We also describe an extension of this algorithm to the case where failed processes are allowed to return to normal states. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(12): 87–96, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1169

Transformations on Regular Nondominated Coteries and Their Applications

A coterie under an underlying set U is a family of subsets of U such that every pair of subsets has at least one element in common, but neither is a subset of the other. A coterie C under U is said to be nondominated (ND) if there is no other coterie D under U such that, for every $Q \in C$, there exists $Q' \in D$ satisfying $Q' \subseteq Q$. We introduce the operation $\sigma$ which transforms a ND coterie to another ND coterie. A regular coterie is a natural generalization of a vote-assignable coterie. We show that any regular ND coterie C can be transformed to any other regular ND coterie D by judiciously applying the $\sigma$ operation to C at most |C|+|D|-2 times. As another application of the $\sigma$ operation, we present an incrementally polynomial-time algorithm for generating all regular ND coteries. We then introduce the concept of a g-regular functional as a generalization of availability. We show how to construct an optimum coterie C with respect to a g-regular functional in O(n3|C|) time, where n =|U|. Finally, we discuss the structures of optimum coteries with respect to a g-regular functional.

Comparing Adjacency Constraint Formulations for Randomly Generated Forest Planning Problems with Four Age-Class Distributions

Abstract Three types of adjacency constraint formulations—pairwise, Type I ND (nondominated), and NOAM (new ordinary adjacency matrix)—were compared on 900 hypothetical, randomly generated, spatially explicit forest management problems with between 50 and 350 stands. Forests were generated using four age-class distributions, nominally called immature, regulated, overmature, and old-growth. Management planning problems with adjacency constraints were formulated for these forests in Model 1 format based on a planning horizon consisting of three 20 yr periods. The problems also included flow constraints and minimum average age requirements for the ending forest. For all age-class distributions, the Type I ND constraint type resulted in significantly lower solution times than either the Pairwise or the NOAM constraint types. NOAM constraints performed better than Pairwise constraints for immature forest problems, but Pairwise constraints performed better than NOAM constraints for overmature and old-growth forest problems. There was no difference between these two constraint types for regulated forest problems. Results also show that the age-class distribution of the forest is one of the most important factors determining the time needed to solve forest management problems with adjacency constraints. In general, the more mature the forest, the harder the problem is to solve. In particular, problems based on the old-growth age-class distribution typically took much longer to solve than comparable problems based on the other age-class distributions. FOR. SCI. 46(3): 423–536.

On the equivalence of two optimization methods for fuzzy linear programming problems

The paper analyses the linear programming problem with fuzzy coefficients in the objective function. The set of nondominated (ND) solutions with respect to an assumed fuzzy preference relation, according to Orlovsky's concept, is supposed to be the solution of the problem. Special attention is paid to unfuzzy nondominated (UND) solutions (the solutions which are nondominated to the degree one). The main results of the paper are sufficient conditions on a fuzzy preference relation allowing to reduce the problem of determining UND solutions to that of determining the optimal solutions of a classical linear programming problem. These solutions can thus be determined by means of classical linear programming methods.

Decompositions of positive self-dual boolean functions

A coterie, which is used to realize mutual exclusion in distributed systems, is a family C of subsets such that any pair of subsets in C has at least one element in common, and such that no subset in C contains any other subset in C. Associate with a family of subsets C a positive Boolean function f c such that f c ( x) = 1 if the Boolean vector x is equal to or greater than the characteristic vector of some subset in C, and 0 otherwise. It is known that C is a coterie if and only if f c is dual-minor, and is a non-dominated (ND) coterie if and only if f c is self-dual. We study in this paper the decomposition of a positive self-dual function into smaller positive self-dual functions, as it explains how to represent and how to construct the corresponding ND coterie. A key step is how to decompose a positive dual-minor function f into a conjunction of positive self-dual functions f 1, f 2,…, f k. In addition to the general condition for this decomposition, we clarify the condition for the decomposition into two functions f 1, and f 2, and introduce the concept of canonical decomposition. Then we present an algorithm that determines a minimal canonical decomposition, and a very simple algorithm that usually gives a decomposition close to minimal. The decomposition of a general self-dual function is also discussed.

Generating and approximating nondominated coteries

A coterie, which is used to realize mutual exclusion in a distributed system is a family C of incomparable subsets such that every pair of subsets in C has at least one element in common. Associate with a family of subsets C a positive (i.e., monotone) Boolean function f/sub c/ such that f/sub c/(x)=1 if the Boolean vector x is equal to or greater than the characteristic vector of some subset in C, and 0 otherwise. It is known that C is a coterie if and only if f/sub c/ is dual-minor, and is a nondominated (ND) coterie if and only if f/sub c/ is self-dual. We introduce an operator /spl rho/, which transforms a positive self-dual function into another positive self-dual function, and the concept of almost-self-duality, which is a close approximation to self-duality and can be checked in polynomial time (the complexity of checking positive self-duality is currently unknown). After proving several interesting properties of them, we propose a simple algorithm to check whether a given positive function is self-dual or not. Although this is not a polynomial algorithm, it is practically efficient in most cases. Finally, we present an incrementally polynomial algorithm that generates all positive self-dual functions (ND coteries) by repeatedly applying p operations. Based on this algorithm, all ND coteries of up to seven variables are computed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>