Worst Solution Research Articles

Size and Shape Design Optimization of Truss Structures Using the Jaya Algorithm

The metaheuristic algorithm is proposed to solve the weight minimization problem of truss structures, considering the shape and sizing design variables. Design variables are discrete and/or continuous. The design of truss structures is optimized by an efficient optimization algorithm called Jaya. The main feature of Jaya is that it does not require setting algorithm-specific parameters. The algorithm has a very simple formulation in which the basic idea is to approach the best solution and escape from the worst solution [6]. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of the Jaya algorithm is demonstrated using two benchmark examples: planar truss 18-bar and spatial truss 39-bar, and compared with results in references.

Comparison of ranking models to evaluate desktop 3D printers in a growing market

While additive manufacturing (AM), commonly known as 3D printing, has been in existence commercially for ∼30 years, desktop 3D printers are a relatively new and rapidly growing market segment. Both well-established AM companies and an increasing number of new enterprises are designing and retailing desktop systems of various sizes, capabilities, and prices. With the abundance of desktop systems now on the market, a consumer may benefit from determining which system best serves their needs. This research highlights differences amongst 45 desktop 3D printers and suggests a method by which to evaluate such differences. For this, a standard part consisting of various geometric features was designed and printed using each system. An updated version of a previously developed quantitative ranking model was utilized to rate the build precision of each system as well as other features, including build volume, size, cost, weight, and layer resolution. In addition, the research team observed part aesthetics and quantified mechanical properties. The criteria evaluated in this ranking model may be modified by each user, to extend this methodology to other desktop AM systems, including professional-grade machines. The results from the model presented in this research were compared with other commonly used ranking methods to help evaluate each technique. These included a simple ascending order rank based model adjusted for ties (1-best and 45-worst), a percentile value based model evaluating the factor contribution values presented in this paper; and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) which evaluated separation measures from ideal and worst solutions. As expected, the comparisons demonstrated that each model had slightly different rankings as compared to the model presented in this paper, with some outliers. Consequently, it was observed that percentile value based models (such as one presented in this paper) provide rankings different from separation value based models (from ideal and worst solutions) such as TOPSIS.

Life cycle assessment of alternative building floor rehabilitation systems

The aim of this study is to quantify and compare the environmental impact from cradle-to-gate of different systems for building floor rehabilitation.The Life Cycle Assessment (LCA) method was used for the evaluation of the potential environmental impact of the production of each floor system in eight categories and a monetisation method was used for the weighting of the results and for their expression into one single indicator. Five functionally equivalent (from a structural standpoint) systems were assessed, including: (i) traditional solutions, such as timber floors and reinforced concrete (RC) slabs; (ii) less conventional solutions, such as beam-and-block and steel–concrete composite slab systems; and (iii) an innovative glass fibre reinforced polymer (GFRP) sandwich panel system. The environmental impacts of these systems are compared in this paper for the first time.It was found out that timber is the most environmentally friendly solution, since it presents the lowest total values in all environmental impact categories (under 1% of the impact of the solution with highest impact in each category, RC or GFRP), except for primary renewable energy consumption, for which the RC solution presents the least consumption. On the other hand, it was found out that: the steel–concrete composite floor is the least environmentally friendly solution in abiotic resource depletion; the RC solution is the least performing option in terms of global warming potential and ozone depletion potential; and the GFRP system presents the worst behaviour in five environmental impact categories – non-renewable energy consumption, photochemical ozone formation potential, acidification potential, eutrophication potential and Eco-costs. However, if the most important inefficiencies identified during the production of the latter floor system are at least partly amended, its impacts would be substantially reduced, particularly regarding the Eco-costs indicator, which considers four environmental categories at once – in that case, the RC solution would become the worst solution and GFRP sandwich panels would have an aggregated result of 80% of the RC. The final part of the paper presents a qualitative assessment of the gate-to-grave performance of the different solutions.

The optimal generator dispatching with uncertain conditions for grid-connected microgrid

Nowadays, the penetration of wind and solar sources is relatively high in Micro Grid. Wind speed and solar radiation forecasting hardly gives an exact value and leads to the values in intervals. Therefore forecasted output powers of these sources are also in the intervals. The constraint on power balance in Micro Grid has the right-hand-side uncertainty, in the interval. So for Micro-Grid in grid connection mode, the presence of the distributed generations based on wind and solar energy sources makes optimal dispatching problems of distributed generations become an uncertainty problem. The optimal solutions for the lower and upper ends of this interval are the best and the worst optimal solution. This paper proposes to treat the above problem as the optimal problem with two objectives: reach the best and the worst solution. The principle of fuzzy set and the Particle Swarm Optimization algorithm will be applied for solving the multi-objective problem. The final optimal value will belong to an interval. Meanwhile, the output power of the swing generator varies to respond to the uncertainty of wind and solar source power. An example of a low-voltage MG with three distributed generators is considered with two cases: connecting to the utility grid via the circuit breaker and via power controller.

Optimal UPQC location in power distribution network via merging genetic and dragonfly algorithm

Nowadays, flexible alternating current transmission system devices, particularly unified power quality conditioner (UPQC) are found to have significant impacts on stability of rising power system. In power systems, several intellectual optimization methods were exploited to position the UPQC. However, those optimization models fail to offer more reliability and feedback signal. Hence, this paper presents a power quality improvement model, which is based on a hybrid algorithm that links genetic algorithm (GA) and DragonFly algorithm (DA). In the current research work, the optimal solution is determined based on the crossover operation of GA in dragonfly algorithm (DA), and hence, the adopted model is named as Genetically Modified DA algorithm. Moreover, the proposed model discovers the optimal location of UPQC device by focusing on the UPQC cost, power losses, and Voltage stability Index. The proposed model is carried out in IEEE 69, and IEEE 33 test bus systems. In addition, the performance of implemented model is distinguished over other conventional models such as artificial bee colony, firefly, grey wolf optimization, whale optimization algorithm, worst solution linked whale optimization algorithm update (WS-WU), GA and DA. The performance of the proposed model is effectively proved by performance and convergence analysis.

Optimal location of UPQC for power quality improvement: novel hybrid approach

PurposeIn general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness.Design/methodology/approachThis paper introduces a power quality enhancement model that is based on a new hybrid optimization algorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithm rider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections.FindingsThrough the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA); 0.09 per cent better than GMDA and WOA; and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models.Originality/valueThis paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors’ knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.

Best-Worst-Play (BWP): A metaphor-less Optimization Algorithm

A novel algorithm which is an ensemble of two metaphor-less algorithms is presented in this paper. The algorithm is inspired by Rao-1 and Jaya algorithms. Since the algorithm always plays around with the best and worst solutions; the algorithm is named as Best-Worst-Play (BWP) algorithm. The algorithm does not require any algorithm specific parameters, however, algorithm control parameters are required. To test the effectiveness and performance of the proposed algorithm, a number of unconstrained and constrained benchmark functions are considered. It is found that proposed algorithm has outperformed several well-established metaphor based algorithms. The proposed BWP algorithm may be used by researchers to solve the unconstrained and constrained optimization problems

Thermal performance of light blocks in a Mediterranean climate

The challenge of reducing energy consumption in buildings is fundamental for the near future, considering the impact of buildings in the overall energy demands. Mediterranean regions have a mild heating season and a hot and dry winter. Its climate is characterized by plentiful solar radiation all along the year and large daily range of temperature during the summer. Thermal inertia and insulation material properties can act in a different way along the day and the year, and the better solution for the summer can be the worst solution for the cooling season. Masonry walls that define the boundary between the interior and exterior are one of the most important components of buildings mainly for energy efficiency. Despite being one of the most common and ancient building materials, it is possible to improve its performance working in units, mortars and finishes. This work aims at studying the effect of materials with different thermal insulation and thermal mass on thermal comfort and energy savings of three types of buildings, two single-family houses and one apartment, in a Mediterranean climate. Thermal behavior of traditional and light solutions is compared and it is found that thermal comfort is similar but light solutions present lower energy consumptions for the three studied house topologies. Otherwise, this study shows that housing topology has more influence namely, when comparing energy needs.

A modified Boltzmann Annealing Differential Evolution algorithm for inversion of directional resistivity logging-while-drilling measurements

Being introduced in the last decade for proactive geosteering, directional resistivity logging-while-drilling (DRLWD) measurements have been used to predict and invert formation boundary, which provides more confidence to geosteering engineers in steering a well towards reservoirs’ sweet spots. However, the strong non-uniqueness of the inversion problems in the data processing in the DRLWD measurements requires a robust algorithm having a global optimization capability. We propose a modified Boltzmann Annealing Differential Evolution (BADE) algorithm to optimize the inversion problem in the DRLWD measurements. The algorithm combines a multi-population Differential Evolution (DE) algorithm with an annealing strategy. Controlled by the annealing temperature, a bad solution may be accepted probabilistically to avoid the local minima. Moreover, different differential strategies are employed at different stages of annealing to improve the convergence as well as to save computation time. The proposed algorithm is suitable for parallel implementation and it can be accelerated drastically. Synthetic and field examples are presented to showcase the effectiveness and accuracy of the BADE algorithm. Compared to the standard DE algorithm, the modified algorithm converges faster and finds global optimization easier. Combined with a multi-layer model, the BADE algorithm can be used to predict multiple formation boundaries and to evaluate the anisotropic formation resistivities, which will be of great help for proactive geosteering and well placement.

Parameter Identification for Photovoltaic Models Using an Improved Learning Search Algorithm

As a renewable energy resource, solar photovoltaics (PV's) possess a promising future. Thus, it is important to simulate, evaluate and control the PV systems. To identify the parameters for the photovoltaic (PV) models, an improved learning search optimization algorithm (ILSA) is proposed in this paper. The proposed ILSA has the following three key features: (i) the constant self-adjustment rate changes with the iteration. (ii) a new part with a self-adaptive weighting of the current best and worst solution is introduced to learning patterns to guide the iterative direction. (iii) a perturbation method is added to avoid the algorithm falling into local optimum. In order to assess the effectiveness of ILSA relative to other state-of-the-art algorithms, single diode, double diode, and PV model are used for test. Our experimental results reveal that the ILSA performs well in terms of the accuracy of optimization solutions and effectiveness.

Biomimetic Platinum Nanozyme Immobilized on 2D Metal-Organic Frameworks for Mitochondrion-Targeting and Oxygen Self-Supply Photodynamic Therapy.

Photodynamic therapy (PDT) as a noninvasive therapy mode has attracted considerable attention in the field of oncotherapy. However, the PDT efficacy is restricted either by the tumor hypoxia environment or the inherent properties of photosensitizers (PSs) including bad water solution, photobleaching, and easy aggregation. Herein, we designed and synthesized a new two-dimensional (2D) metal-organic framework, Sm-tetrakis(4-carboxyphenyl)porphyrin (TCPP) nanosheets, by assembling transition metal ions (Sm3+) and PSs (TCPP), on which the catalase (CAT)-mimicking platinum nanozymes were then in situ grown for sufficient oxygen supply during PDT. The prepared Sm-TCPP with nanoplate morphology (∼100 nm in diameter) and ultrathin thickness (<10 nm) showed significantly enhanced 1O2 generation capacity due to the improved physicochemical properties and the enhanced intersystem crossing from heavy Sm nodes. More importantly, the CAT-mimicking Pt nanozyme on the Sm-TCPP nanosheets could effectively convert over-expressed H2O2 in the tumor microenvironment into O2 to relieve tumor hypoxia. Further, the triphenylphosphine (TPP) molecule was introduced to Sm-TCPP-Pt to develop a mitochondrion-targeting and O2 self-supply PDT system. The in vitro and in vivo experimental results based on the MCF-7 breast cancer model revealed that Sm-TCPP-Pt/TPP could relieve tumor hypoxia and the generated reactive oxygen species nearby intracellular mitochondria significantly induced cell apoptosis. This study offers an engineering strategy to integrate 2D PS-based metal-organic frameworks and nanozymes into a nanoplatform to surmount the pitfalls of traditional PDT.

A population-based iterated greedy algorithm for no-wait job shop scheduling with total flow time criterion

The no-wait job shop where no waiting time is allowed between two successive operations of a job has a strong industrial background, especially in steel-making industry and concrete manufacturing. This study formulates the no-wait job shop problem with a total flow time criterion based on time difference and decomposes the problem into timetabling and sequencing subproblems. Several timetabling methods are designed for the total flow time criterion to generate a sequence timetable. By adopting favourable features of the iterated greedy algorithm, the population-based iterated greedy (PBIG) algorithm for the sequencing subproblem is proposed. The individuals in the population evolve by means of a destruction and construction perturbator and an insertion-based local search. In each iteration, a tournament selection is designed to replace a relatively worse solution. In order to generate starting solutions with a certain quality and diversity, the Nawaz–Enscore–Ham-based heuristics for flow shop scheduling are extended in no-wait job shops. In computational experiments based on well-known benchmark instances, timetabling methods are investigated, and it is shown that the left timetabling is superior to other timetabling methods for the total flow time minimisation. Computational results also show that the proposed algorithm significantly outperforms several state-of-the-art metaheuristics, and it could be applicable to practical production environment.

Block layer decomposition schemes for training deep neural networks

Deep Feedforward Neural Networks' (DFNNs) weights estimation relies on the solution of a very large nonconvex optimization problem that may have many local (no global) minimizers, saddle points and large plateaus. As a consequence, optimization algorithms can be attracted toward local minimizers which can lead to bad solutions or can slow down the optimization process. Furthermore, the time needed to find good solutions to the training problem depends on both the number of samples and the number of variables. In this work, we show how Block Coordinate Descent (BCD) methods can be applied to improve performance of state-of-the-art algorithms by avoiding bad stationary points and flat regions. We first describe a batch BCD method ables to effectively tackle the network's depth and then we further extend the algorithm proposing a \textit{minibatch} BCD framework able to scale with respect to both the number of variables and the number of samples by embedding a BCD approach into a minibatch framework. By extensive numerical results on standard datasets for several architecture networks, we show how the application of BCD methods to the training phase of DFNNs permits to outperform standard batch and minibatch algorithms leading to an improvement on both the training phase and the generalization performance of the networks.

Modified Antipredatory Particle Swarm Optimization for Dynamic Economic Dispatch with Wind Power

A modified antipredatory particle swarm optimization (MAPSO) algorithm with evasive adjustment behavior is proposed to solve the dynamic economic dispatch problem of wind power. The algorithm adds the social avoidance inertia weight to the conventional antipredatory particle swarm optimization (APSO) speed update formula. The size of inertia weight is determined by the distance between the global worst particle and other particles. After normalizing the distance, the inertia weight is controlled within the ideal range by using the characteristics of sigmoid function and linear decreasing method, which improves the ability of particles to avoid the worst solution. Then, according to the characteristics of the acceleration coefficient which can adjust the local and global searching ability of particles, acceleration coefficients of nonlinear change strategy is proposed to improve the searching ability of the algorithm. Finally, the proposed algorithm is applied to several benchmark functions and power grid system models, and the results are compared with those reported using other algorithms, which prove the effectiveness and superiority of the proposed algorithm.

Cluster structure prediction via revised particle-swarm optimization algorithm

The global minimization of cluster structure at the atomic level is emerging as a state-of-the-art approach to accelerate the functionality-driven discovery of cluster-based materials. In this work, we have developed a method for global optimization of Lennard-Jones (LJ), elemental metal and bimetallic clusters through revised particle swarm optimization (RPSO) algorithm within random learning procedure, competition operation and confusion mechanism. The approach requires only size and chemical composition for a given cluster to predict stable or metastable structures at given external conditions. Random learning procedure significantly improves the performance of RPSO such as converge rate and optimization efficiency. Moreover, competition operation can ensure the superiority of outstanding individuals and accept the bad solution with a certain probability. Confusion mechanism allows the clusters to fly in a wider space and makes large-scale optimization possible. Results of optimization based on test functions show that the convergence of RPSO is much faster and more reliable than several other algorithms. In addition, RPSO can also perform well on optimization of Lennard-Jones clusters, Pt and Pt–Pd clusters with various sizes and compositions. The high success rate of RPSO demonstrates the reliability of this methodology and provides crucial insights for understanding the rich and complex structures of clusters.

A Novel SFLA for Re-entrant Hybrid Flow Shop Scheduling

In order to optimize makespan and total tardiness for re-entrant hybrid flow shop scheduling problems simultaneously, this paper proposes a novel Shuffled Frog Leaping Algorithm. The algorithm relies on the lexicographic method to process total tardiness and makespan. A suitable crossover operator is introduced. During the searching process of memeplex, instead of worst solution, multiple best solutions are used to generate new solutions. Further with that, the process of generating random solution is replaced with the neighborhood search. Then as indicated by the simulation based on a group of instances, the novel SFLA is superior in addressing hybrid flow shop scheduling problem.

Community Knowledge and Behavior Towards Unwanted Pregnancy in Eight Provinces of Sumatra: Between Religious, Customary and State Norms

&lt;p&gt;Based on field observations and experiences in assisting women by the PERMAMPU Consortium, there are still many women who face unwanted pregnancy (KTD). Therefore it is needed research to find out the needs of women related to KTD. This article focuses on three things, namely (1) understanding and knowledge of informants about KTD; (2) perception of the best and the worst solutions for KTD cases; (3) women’s behavior related to KTD. The study uses three categories of KTD: KTD experienced by married women, KTD experienced by unmarried women, and KTD experienced by victims of acts of violence. This article is written from the 2014 PERMAMPU Consortium research report entitled The &lt;em&gt;Compilation of Unwanted Pregnancy Research Reports&lt;/em&gt;/&lt;em&gt;KTD in Eight Provinces, Sumatra Island.&lt;/em&gt; The study used a qualitative approach with data collection conducted through interviews and FGDs. This paper shows that there are similarities and differences in the understanding, perceptions, behavior of women and society in relation to KTD. Various forms of rules and norms generally view pregnancy as natural, so that it is always desirable, have controlled women and society in behaving and handling KTD. There are various forms of KTD and various forms of coping methods that are not always in line with existing rules and are generally done secretly. Thus, women who experience unwanted pregnancy need recognition of their problems, including their voices and needs and supported to make decisions for themselves. &lt;/p&gt;

Surrogate-assisted classification-collaboration differential evolution for expensive constrained optimization problems

Expensive Constrained Optimization Problems (ECOPs) widely exist in various scientific and industrial applications. Surrogate-Assisted Evolutionary Algorithms (SAEAs) have recently exhibited great ability in solving these expensive optimization problems. This paper proposes a Surrogate-Assisted Classification-Collaboration Differential Evolution (SACCDE) algorithm for ECOPs with inequality constraints. In SACCDE, the current population is classified into two subpopulations based on certain feasibility rules, and a classification-collaboration mutation operation is designed to generate multiple promising mutant solutions by not only using promising information in good solutions but also fully exploiting potential information hidden in bad solutions. Afterwards, the surrogate is utilized to identify the most promising offspring solution for accelerating the convergence speed. Furthermore, considering that the population diversity may decrease due to the excessive incorporation of greedy information brought by the classified solutions, a global search framework that can adaptively adjust the classification-collaboration mutation operation based on the iterative information is introduced for achieving an effective global search. Therefore, the proposed algorithm can strike a well balance between local and global search. The experimental results of SACCDE and other state-of-the-art algorithms demonstrate that the performance of SACCDE is highly competitive.

Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks

This paper presents a detailed comparison between 3 methods for emulating CPU-intensive reactive transport models (RTMs): Gaussian processes (GPs), polynomial chaos expansion (PCE), and deep neural networks (DNNs). State-of-the-art open source libraries are used for each emulation method while the CPU-time incurred by one forward run of the considered RTMs varies from 1 h to between 1 h and 30 min and 5 days. Besides direct emulation of the simulated uranium concentration time series, replacing the original RTM by its emulator is also investigated for global sensitivity analysis (GSA), uncertainty propagation, and probabilistic calibration using Markov chain Monte Carlo (MCMC) sampling. The selected DNN is found to be superior to both GPs and PCE in reproducing the input–output behavior of the considered 8-dimensional and 13-dimensional CPU-intensive RTMs. This even though the used training sets are small, from 75 to 500 samples. Furthermore, the two used PCE variants, standard PCE and sparse PCE (sPCE), appear to always provide the least accuracy while not differing much in performance. As a consequence of its better emulation capabilities, the DNN method outperforms the two other methods for uncertainty propagation. For the GSA application, the DNN and GP methods offer equally good approximations to the true first-order and total-order Sobol’ sensitivity indices while PCE does less well. Most surprisingly, despite its superior emulation skills, the DNN approach leads to the worst solution of the considered synthetic inverse problem which involves 1224 measurement data with low noise. This apparently contradicting behavior of the used DNN is at least partially due to the small but complicated deterministic noise that affects the DNN-based predictions. Indeed, this complex error structure can drive the emulated solutions far away from the true posterior distribution in case of high-quality measurement data. Among the considered 3 methods, only the GP method allows for retrieving emulated posterior solutions that jointly (1) fit the high-quality measurement data to the appropriate noise level (log-likelihood value) and (2) most closely fit the true model parameter values. Overall, our findings indicate that when the available training set is relatively small (75 to 500 input-output examples) and fixed beforehand, PCE is not the best choice for emulating CPU-intensive RTMs. Instead, GPs or DNNs should be preferred. However, a DNN can deliver overly biased model calibration results. In contrast, the GP method performs fairly well across all considered tasks: direct emulation, global sensitivity analysis, uncertainty propagation, and calibration.

Optimal operation of transmission power networks by using improved stochastic fractal search algorithm

This paper presents the application of an improved stochastic fractal search algorithm (ISFSA) for optimizing five single objectives of optimal power flow (OPF) problem and satisfying all constraints consisting of operating limits of electric components, power balance and load voltage magnitude limits. The proposed ISFSA is formed by implementing three improvements on the conventional stochastic fractal search algorithm (SFSA). The first improvement cancels one ineffective formula but keeps another one in diffusion process. The second improvement selects some worst solutions in the first update and some best solutions in the second update for producing new solutions. In the third improvement, a proposed technique is applied for carrying out the update processes. Comparisons of obtained results from three standard IEEE power systems indicate that the proposed method is superior to SFSA in terms of optimal solution quality, execution speed as well as success rate. The performance comparisons with other existing methods available in previous studies also lead to the conclusions that the proposed method can reach lower generation fuel cost, smaller total power losses, less amount of emission, better voltage profile and faster execution process. As a result, it can be recommended that the proposed ISFSA should be used for OPF problem in high-voltage power system field.