Explore the vast range of titles available.

In this study, critical peak pricing with load control (CPPLC), recently announced by Federal Energy and Regulatory Commission, is investigated in a cost-emission-based unit commitment (UC) problem. In order to be easily implementable with available real market solvers, the non-linear, non-convex problem formulation is converted to multi-objective mixed integer linear programming (MMILP). The MMILP problem is then solved through a new modified ε-constraint multi-objective optimisation method. Moreover, UC is applied not only to schedule the status of the generating units but also to determine both price deviations and load profile provided by CPPLC program. Finally, the conventional 10-unit test system is employed to indicate the applicability of the proposed method through several case studies.

The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable‐based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro‐based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi‐objective optimisation method proposed in this paper for optimal integration of renewable‐based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33‐bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24‐hr time‐varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

Owing to the continuously increased electricity demands and power transactions, power systems are becoming more vulnerable to insecurity, generally incurred by overutilised transmission facilities or any contingency. In order for existing transmission networks to accommodate more power transfers with less network expansion cost, proper installation of thyristor controlled series compensator (TCSC) is validated to be one of the most ‘promising options’. The multi-objective optimal TCSC installation strategy proposed in the study first applies the performance index sensitivity factor technique to investigate which lines are most necessary for TCSC installation and with the lines specified for TCSC installation and the multi-objective function consisting of maximum system loadability and minimum TCSC installation cost, the problem to determine the capacity for each TCSC installation is then formulated as a multi-objective optimisation problem and solved by using the fitness sharing multi-objective particle swarm optimisation method. Finally, in the Pareto front set obtained, the solution with the TCSC installations that can make the power system provide the required loadability with biggest utilisation index value is recommended. The modified IEEE-14 buses, IEEE-118 buses systems and a practical power system are used to validate the performance of the proposed method.

In almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. Methods such as NSGA-II, SPEA2, SMS-EMOA, MOPSO, and MOEA/D became standard solvers when it comes to solving multiobjective optimization problems. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization. The aim is to provide a starting point for researching in this active area, and it should also help the advanced reader to identify open research topics.

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.

Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-dominated sorting genetic algorithm II, differential evolution for multiobjective optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, multiobjective flower pollination algorithm, multiobjective bat algorithm, multiobjective gray wolf optimizer, multiobjective multiverse optimization, multiobjective water cycle optimizer, success history-based adaptive multiobjective differential evolution, success history-based adaptive multiobjective differential evolution with whale optimization, multiobjective salp swarm algorithm, real-code population-based incremental learning and differential evolution, unrestricted population size evolutionary multiobjective optimization algorithm, and multiobjective jellyfish search optimizer. In addition, the adaptive chaos control method is employed for the above-mentioned algorithms to estimate the probabilistic constraints effectively. This comparative analysis reveals the critical technologies and enormous challenges in the RBDO field. It also offers new insight into simultaneously dealing with the multiple conflicting design objectives and probabilistic constraints. Also, this study presents the advantage and future development trends or incurs the increased challenge of researchers to put forward an effective multiobjective RBDO algorithm that assists the complex engineering system design.

Multiobjective optimization assumes a one-to-one mapping between decisions and objective space, however, this is not always the case. When many variables have the same or equivalent objective value, a multimodal multiobjective issue develops in which more than one Pareto Set (PS) maps to the same Pareto Front (PF). Evolutionary computing research into multimodal multiobjective optimization issues has increased (MMOPs). This paper proposed an enhanced multimodal multiobjective genetic algorithm to crack MMOPs using a special crowding distance calculation (ESNSGA-II). This special crowding distance calculation can consider the diversity of the decision space while paying attention to the diversity of the object space. Then, a unique crossover mechanism is established by combining the simulated binary crossover (SBX) method with the capacity of Pareto solutions to generate offspring solutions. The balance between convergence and diversity in both decision space and object space can be guaranteed synchronously, and PS distribution and PF accuracy may both be enhanced. The proposed ESNSGA-II uses the CEC2020 benchmarks MMF1-MMF8 to assess its properties. Comparing the ESNSGA-II to other recently established multimodal multiobjective evolutionary techniques demonstrates that it is capable of efficiently searching numerous PSs of MMOPs. Finally, the suggested ESNSGA-II is used to address a real MMOP problem of pulmonary hypertension detection via arterial blood gas analysis. The statistical analysis reveals that the suggested ESNSGA-II algorithm outperforms other algorithms on this MMOP, and so may be considered a possible tool for pulmonary hypertension. •The ESNSGA-II is presented for MMOPs.•Performance of the ESNSGA-II is enhanced by special crowding distance.•The balance between convergence and diversity of decision and object space can be guaranteed.•The efficacy of ESNSGA-II is shown on benchmarks and is much superior to other methods.•ESNSGA-II may be treated as tool for assist PH diagnosis.

Multiobjective optimization approaches have allowed the improvement of technical features in industrial processes, focusing on more accurate approaches for solving complex engineering problems and support decision-making. This paper proposes a hybrid approach to optimize the 3D printing technology parameters, integrating the design of experiments and multiobjective optimization methods, as an alternative to classical parametrization design used in machining processes. Alongside the approach, a multiobjective differential evolution with uniform spherical pruning (usp-MODE) algorithm is proposed to serve as an optimization tool. The parametrization design problem considered in this research has the following three objectives: to minimize both surface roughness and dimensional accuracy while maximizing the mechanical resistance of the prototype. A benchmark with non-dominated sorting genetic algorithm II (NSGA-II) and with the classical sp-MODE is used to evaluate the performance of the proposed algorithm. With the increasing complexity of engineering problems and advances in 3D printing technology, this study demonstrates the applicability of the proposed hybrid approach, finding optimal combinations for the machining process among conflicting objectives regardless of the number of decision variables and goals involved. To measure the performance and to compare the results of metaheuristics used in this study, three Pareto comparison metrics have been utilized to evaluate both the convergence and diversity of the obtained Pareto approximations for each algorithm: hyper-volume (H), g-Indicator (G), and inverted generational distance. To all of them, ups-MODE outperformed, with significant figures, the results reached by NSGA-II and sp-MODE algorithms.

In this paper, we propose a projected subgradient method for solving constrained nondifferentiable quasiconvex multiobjective optimization problems. The algorithm is based on the Plastria subdifferential to overcome potential shortcomings known from algorithms based on the classical gradient. Under suitable, yet rather general assumptions, we establish the convergence of the full sequence generated by the algorithm to a Pareto efficient solution of the problem. Numerical results are presented to illustrate our findings.

Generation maintenance scheduling (GMS) is one of the most important scheduling problems in the restructured power systems. The maintenance time interval of generation units is the crucial factor of GMS for an operation lifespan of generation units, particularly within the smart grid which needs high reliability. Accordingly, this study proposes a multi‐objective‐GMS (MO‐GMS) optimisation model for maintenance scheduling of generation units based on the global criterion approach, adopting a suitable compromise function. The proposed MO‐GMS model determines the maintenance intervals, aims to maximise both the generation company's (GenCo's) financial returns from selling electricity and the system reserve at every time interval from the independent system operator (ISO) standpoint. This method searches the optimal maintenance weeks for each generation unit, considering the objectives of both GenCo and ISO, simultaneously. The proposed MO‐GMS model is formulated as a mixed‐integer non‐linear programming problem and examined on the IEEE 24‐bus and IEEE 118‐bus test systems. The success of the proposed multi‐objective model is validated by comparing the obtained results with intelligent optimisation algorithms.