Conventional Single-objective Optimization Research Articles

Vibration based single-objective finite element model updating using cooperative game theory approach

This paper introduces a novel approach that focuses on application of cooperative game theory model for automated finite element model updating. The approach involves transforming the conventional single-objective optimization finite element model updating problem into a game theory problem. Among the different alternatives, a cooperative game model is considered to solve the transformed updating problem. This new approach overcomes the limitations of the conventional single-objective optimization formulation, which include challenges related to selecting appropriate weighting factor of residuals and analysing their influence on optimization results. To demonstrate the effectiveness of the proposed approach, an ultralight fibre reinforced polymer footbridge is considered. The numerical model of the footbridge is updated using the experimentally determined inherent properties (natural frequencies and mode shapes) using the proposed approach. A comparison with the conventional sensitivity-based weighted single objective method reveals that the inclusion of the weighting factor in the optimization process under the cooperative game model method allows for more efficient solution of the single-objective updating optimization problem, while maintaining accuracy and quality in the updated model.

A review on the microgrid sizing and performance optimization by metaheuristic algorithms for energy management strategies

A growing concern over climate change and the depletion of conventional energy resources have led to the urgent need for sustainable and resilient energy solutions. The optimization of the size of renewable sources is crucial to maximizing their effectiveness. In contrast to conventional single-objective optimization, the multi-objective technique aims to achieve a trade-off between energy cost and power supply reliability. Due to this need, microgrids (MG) have emerged as a promising paradigm, allowing for localized and decentralized energy generation and distribution. Consequently, the conventional techniques for modelling and optimizing exhibit numerous limitations as the power grid continues to produce substantial volumes of high-dimensional and diverse data types. This review paper examines the use of metaheuristic algorithms in the context of multi-objective energy optimization for hybrid renewable energy-integrated microgrids. A comparative analysis of diverse metaheuristic algorithms for microgrid optimization is provided in this paper, which emulates natural phenomena, such as evolutionary processes and swarm dynamics. Based on the findings of case studies, it can be concluded that trade-offs exist between various objectives, eventually leading to the development of both resilient and efficient microgrid designs. By reviewing sustainable energy solutions, and advocating microgrids as viable alternatives to conventional centralized power systems, the review enhances the advancement of sustainable energy solutions.

Many-Objective Gradient-Based Optimizer to Solve Optimal Power Flow Problems: Analysis and Validations

The growing energy demand and environmental consciousness provoke the conventional single-objective optimization framework no longer satisfies new power system planning and control requirements. The number of optimization objectives being considered in power system optimization is increasing, necessitating the development of many-objective Optimal Power Flow (OPF) problems and the development of solution methods. In this paper, the fitness functions for the Many-Objective OPF (MaO-OPF) problem have been formulated, and a new Many-Objective Gradient-Based Optimizer (MaOGBO) based on reference point strategy is proposed to solve the MaO-OPF problem. The objectives of the MaO-OPF problem is to minimize the Reactive Power Loss (RPL), Active Power Loss (APL), Voltage Magnitude Deviation (VMD), Voltage Stability Indicator (VSI), Total Emission (TE), and the Total Fuel Cost (TFC) by satisfying different complex equality and inequality constraints. In the proposed MaOGBO, a reference point strategy is employed to acquire evenly spread Pareto-optimal solutions in each objective space. In order to improve the effectiveness of Pareto solutions, a mixed-multi-constraint handling approach is also implemented. In order to deal with the complex non-linear constraints, the process utilizes both the repair technique and penalty function. Besides, a fuzzy-based membership function strategy is also applied to locate the Best Compromise Solution (BCS) from the Pareto-optimal analytical solution. In order to validate the effectiveness of the proposed MaOGBO, DTLZ and MaF benchmark test suites are considered. In addition, a standard Institute of Electrical and Electronics Engineers (IEEE) bus test systems, such as IEEE-30/IEEE-57/IEEE-118 with different case studies, are also considered to assess the performance of the proposed algorithm. The obtained results are compared with other state-of-the-art algorithms, and the proposed MaOGBO proves the superiority over other competitors for most of the selected problems, including MaO-OPF. Finally, the proposed MaOGBO is also validated on large-scale Algerian 59-bus power systems and proved its superiority in handling realistic systems. This research is further supported up with extra online service and guidance at https://premkumarmanoharan.wixsite.com/mysite.

Multiobjective Multicast DSR Algorithm for Routing in Mobile Networks with Cost, Delay, and Hop Count

Tremendous evaluation of wireless mobile communication needs more efficient algorithms for communication systems. The use of conventional single-objective optimization algorithms may be unsuitable for real applications, because they act to the detriment of the rest of the performance parameters like lifetime network, delay, cost, and hop count; for this reason, multiobjective is needed. This paper presents performance evaluation and compares between the Multicast MDSR and MAODV with MACO. The proposed MDSR is concerned with change of the route discovery phase, where the route selection is based on the shortest path of route reply packets on the route with calculating the number of hop counts. Also, this article compares our MDSR modification with the evaluation algorithm based on Ant Colony Optimization (ACO), which finds the best path and multicast tree optimizes total weight (cost, delay, and hop count) of the multicast tree using multiobjective. Experimental results proved that the proposed MDSR algorithm is more efficient than MAODV and MACO in the total weight (cost, delay, and hop count), respectively. Moreover, the MACO outperforms MAODV for multicast routing problem.

Multi-objective robust optimization of active distribution networks considering uncertainties of photovoltaic

The conventional single objective optimization cannot meet the requirements for the optimal operation of active distribution networks (ADNs) with multiple uncertainties brought by the distributed generation (DG), diverse demand responses and flexible topologies etc. In addition, the contradictions among multiple objectives, the tradeoff between the robustness, conservative, and deterioration of performance, as well as the computation cost have challenged the solution of multi-objective optimization, in particular for the ADNs with multiple uncertainties along with complicated operation scenarios. Considering the uncertainties of distributed photovoltaic (PV), a multi-objective robust optimization strategy for ADNs is proposed in this paper to maximize the utilization of renewables and to minimize the network losses at the same time. By constructing the uncertainty set of predicted PV’s maximum outputs, the extreme scenarios of the uncertainty set are chosen to establish constraints, and reduce the dimension of calculation. Then, the multi-objective optimization algorithm can obtain the Pareto fronts under various ranges of uncertainty sets while satisfying the physical and operating constraints of the ADNs, the influence of various ranges of uncertainty set on the Pareto front is taken into consideration. The effectiveness and robustness of the proposed model are verified on a modified IEEE 33 bus system, and the scalability is tested on a 141 bus distribution network.

Spectral-Energy Efficiency Trade-Off-Based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems

Energy efficiency (EE) and spectral efficiency (SE) are two of the key performance metrics in future wireless networks, covering both design and operational requirements. For previous conventional resource allocation techniques, these two performance metrics have been considered in isolation, resulting in severe performance degradation in either of these metrics. Motivated by this problem, in this paper, we propose a novel beamforming design that jointly considers the trade-off between the two performance metrics in a multiple-input single-output non-orthogonal multiple access system. In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good trade-off between the two performance metrics. However, this MOO problem is not mathematically tractable and, thus, it is difficult to determine a feasible solution due to the conflicting objectives, where both need to be simultaneously optimized. To overcome this issue, we exploit a priori articulation scheme combined with the weighted sum approach. Using this, we reformulate the original MOO problem as a conventional single objective optimization (SOO) problem. In doing so, we develop an iterative algorithm to solve this non-convex SOO problem using the sequential convex approximation technique. Simulation results are provided to demonstrate the advantages and effectiveness of the proposed approach over the available beamforming designs.

Thermodynamic analysis and optimization of an irreversible Ericsson cryogenic refrigerator cycle

Abstract Optimum ecological and thermal performance assessments of an Ericsson cryogenic refrigerator system are investigated in different optimization settings. To evaluate this goal, ecological and thermal approaches are proposed for the Ericsson cryogenic refrigerator, and three objective functions (input power, coefficient of performance and ecological objective function) are gained for the suggested system. Throughout the current research, an evolutionary algorithm (EA) and thermodynamic analysis are employed to specify optimum values of the input power, coefficient of performance and ecological objective function of an Ericsson cryogenic refrigerator system. Four setups are assessed for optimization of the Ericsson cryogenic refrigerator. Throughout the three scenarios, a conventional single-objective optimization has been utilized distinctly with each objective function, nonetheless of other objectives. Throughout the last setting, input power, coefficient of performance and ecological function objectives are optimized concurrently employing a non-dominated sorting genetic algorithm (GA) named the non-dominated sorting genetic algorithm (NSGA-II). As in multi-objective optimization, an assortment of optimum results named the Pareto optimum frontiers are gained rather than a single ultimate optimum result gained via conventional single-objective optimization. Thus, a process of decision making has been utilized for choosing an ultimate optimum result. Well-known decision-makers have been performed to specify optimized outcomes from the Pareto optimum results in the space of objectives. The outcomes gained from aforementioned optimization setups are discussed and compared employing an index of deviation presented in this work.

Determination of optimal operating conditions for a polymer electrolyte membrane fuel cell stack: optimal operating condition based on multiple criteria

SUMMARY A methodology for optimal control of the polymer electrolyte membrane fuel cell (PEMFC) with multiple criteria is presented here. In this regard, thermoelectric objectives and thermoeconomic objective are considered, simultaneously. The proposed fuel cell is a 1200 W Ballard PEMFC namely Nexa™ power module. The net power density and exergetic efficiency of the PEMFC are maximized, and the unit cost of the generated power is minimized in a multi-objective optimization procedure using the NSGA-II (non-dominated sorting genetic algorithm). Operating temperature and pressure, air stoichiometric coefficient at the cathode and the current density are considered as controlling parameters in order to acquire optimal performance of the PEMFC. A set of optimal solution namely the Pareto frontier is obtained, and a final optimal solution is selected from available solutions located on the Pareto frontier using the fuzzy decision-making process based on the Bellman–Zadeh approach. Results are compared with corresponding results obtained previously in single objective optimization scenarios. It has been shown that the optimal operating condition obtained based on the multiple criteria approach has least deviation from the ideal features of the fuel cell in comparison to the corresponding optimal solution obtained in conventional single-objective optimization approaches. Copyright © 2013 John Wiley & Sons, Ltd.

A Design Support Through Multi-Objective Optimization Aware of Subjectivity of Value System

As a human-computer interface associated with optimal design of artificial products, it is essential for designers to model adequately their value system having inherently a multi-attributed nature. It aims eventually at developing a use-adaptive intelligent system in manufacturing. With this point of view, in this paper, we have developed a novel design method for multi-objective optimization (MOP) in terms of soft computing. The method has very flexible modeling ability of value function that is likely to fluctuate depending on the decision environment. Actually we have given methods to revise certain inconsistencies in the subjective judgments at the stage of value function modeling. Additionally its formulation allows us to apply any conventional single-objective optimization methods readily for obtaining the preferentially optimal solution. Furthermore, we can employ it regardless of the nature of the model under consideration, i.e., physical model or metamodel. After outlining the solution procedure, effectiveness of the proposed method is examined through an illustrative case study.