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Reliability and maintainability are the key system effectiveness measures in process and manufacturing industries, and treatment plants, especially in E-waste management plants. The present work is proposed with a motto to develop a stochastic framework for the e-waste management plant to optimize its availability integrated with reliability, availability, maintainability, and dependability (RAMD) measures and Markovian analysis to estimate the steady-state availability of the E-waste management plant. In the analysis an effort is also made to identify the best performing algorithm for availability optimization of the e-waste plant. A stochastic model for a particular plant is developed and its availability is optimized using various metaheuristic approaches like a genetic algorithm (GA), particle swarm optimization (PSO), and differential evolutions (DE). The most sensitive component is identified using RAMD methodology while the effect of deviation in various failure and repair rates are observed by the proposed model. The failure and repair rates follow an exponential distribution. All time-dependent random variables are statistically independent. A novel stochastic model is presented for an e-waste management plant and optimum availability is obtained using metaheuristic approaches. The proposed methodology is not so far discussed in the reliability analysis of process industries. The numerical results of the proposed model compared to identify the most efficient algorithm. It is observed that genetic algorithm provides the maximum value (0.92330969) of availability at a population size 2500 after 500 iterations. PSO algorithm attained the maximum value (0.99996744) of availability just after 50 iterations and 100 population size. So, its rate of convergence is faster than GA. The optimum value of availability is 0.99997 using differential evolution after 500 iterations and population size of more than 1000. These findings are very beneficial for system designers. The proposed methodology can be utilized to find the reliability measures of other process industries. [ABSTRACT FROM AUTHOR]

Population-based optimization algorithms are popular tools for optimization, influenced by two interactive factors: landscape bias, which guides the population toward better objective function values, and structural bias, which directs the population to a specific location in the search domain. Structural bias can impede the exploration of algorithms, compelling the population to revisit a particular area of the search domain, which leads to increased computing costs without yielding any new information. In this study, we present a comprehensive survey of structural bias. Identifying this bias in a general algorithm is challenging. We propose the 'Generalized Signature Test' as a methodology to detect and analyze the structural bias in population-based optimization algorithms. Numerical simulations are performed using six well-known metaheuristic algorithms: Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimizer, Whale Optimization Algorithm, and Harris Hawks Optimization. Experimental results suggest that Differential Evolution displays the least bias toward the center of the search domain, whereas the Harris Hawks Optimization exhibits a significant axial-diagonal bias toward the origin. Our findings indicate that the proposed method is both efficient and straightforward for assessing algorithmic structural bias. The Generalized Signature Test provides an in-depth analysis and understanding of biases, tailor-made for thorough bias behavior investigations. The Generalized Signature Test can identify bias in established algorithms and evaluate bias in emerging algorithms at their early development stages. [ABSTRACT FROM AUTHOR]

Highlights • Monthly precipitation was predicted using soft computing methods including ANFIS with GA, ACOR, PSO and DE. • Suggested algorithms improved the ANFIS performance for modeling the precipitation. • Suggested methods decreased the event of trapping in local optima. • ANFIS-ACOR model with least error and most correlation showed highest performance in monthly precipitation forecast. Abstract Existent algorithms to train adaptive neuro-fuzzy inference system (ANFIS) such as gradient descent and least-square methods are useful, but they have problems such as trapping in local optimum as well as high-volume of computations. This paper examines the application of genetic algorithm (GA), ant colony optimization for the continuous domain (ACO R), particle swarm optimization (PSO) and differential evolution (DE) for improving the performance of ANFIS models in simulating monthly rainfall magnitudes. Monthly rainfall records of nine foothills to arid weather stations of Isfahan province, central Iran were used for evaluating the proposed methodology. The correlation coefficient and non-linear sensitivity analysis were performed on the available rainfall records. Monthly rainfall modelling was carried out based on three input groups and used separately in five applied models (e.g. ANFIS, ANFIS-PSO, ANFIS-ACO R , ANFIS-DE, and ANFIS-GA). The obtained results showed that proposed hybrid models had better accuracy than the simple ANFIS model in escaping local optima. The best model was ANFIS-ACO R (with R2 = 0.92, RMSE = 2.73 mm and SI = 0.26), while sole ANFIS model (with R2 = 0.33, RMSE = 9.98 mm and SI = 0.90) presented the worst output. Moreover, the obtained results showed that: (1) Coupling the optimization algorithms with ANFIS can improve its performance in modeling the monthly rainfall time series; (2) the correlation coefficient and sensitivity analysis of input data provided a valid input selection that could improve the model accuracy with neglecting the redundant input parameters. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the application of three well-known multi-objective optimization algorithms to water distribution network (WDN) optimum design has been considered. Non-dominated sorting genetic algorithm II (NSGA-II), Multi-objective differential evolution (MODE) and Multi-objective particle swarm optimization (MOPSO) algorithms are applied to benchmark mathematical test function problems for evaluating the performance of these algorithms. The Accuracy and computational runtime are the two indicators used for the comparison of these three algorithms. The optimization results of mathematical test functions show that all three algorithms were able to accurately produce Pareto Front, but the computational time of MODE algorithm to achieve the optimal solutions is lower than the two other algorithms. Then, the discussed algorithms have been used to optimize the WDN design problem. Comparison of the generated solutions on the Pareto Front for WDN design shows that the obtained Pareto Front of MODE is more accurate and faster. [ABSTRACT FROM AUTHOR]

Abstract Considered as cost-efficient, reliable and aesthetic alternatives to the conventional retaining structures, Mechanically Stabilized Earth Walls (MSEWs) have been increasingly used in civil engineering practice over the previous decades. The design of these structures is conventionally based on engineering guidelines, requiring the use of trial and error approaches to determine the design variables. Therefore, the quality and cost effectiveness of the design is limited with the effort, intuition, and experience of the engineer while the process transpires to be time-consuming, both of which can be solved by developing automated approaches. In order to address these issues, the present study introduces a novel framework to optimize the (i) reinforcement type, (ii) length, and (iii) layout of MSEWs for minimum cost, integrating metaheuristic optimization algorithms in compliance with the Federal Highway Administration guidelines. The framework is conjoined with optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE) and tested with a set of benchmark design problems that incorporate various types of MSEWs with different heights. The results are comparatively evaluated to assess the most effective optimization algorithm and validated using a well-known MSEW analysis and design software. The outcomes indicate that the proposed framework, implemented with a powerful optimization algorithm, can effectively produce the optimum design in a matter of seconds. In this sense, DE algorithm is proposed based on the improved results over GA, PSO, and ABC. Highlights • An automated framework is developed to design Mechanically Stabilized Earth Walls. • The design procedure is formulated as a mixed discrete-continuous variable problem. • The constraints are derived from stability and construction feasibility requirements. • Experiments yield cost efficient yet practically feasible design solutions. • Among many optimization algorithms, Differential Evolution is proposed for the problem. [ABSTRACT FROM AUTHOR]

Customer satisfaction is crucially affected by energy consumption in mobile devices. One of the most energy-consuming parts of an application is images. This paper, first, investigates that there is a correlation between energy consumption and image quality as well as image file size. Therefore, these two can be considered as a proxy for energy consumption. In the next step, we focused on proposing a multi-objective strategy to enhance image quality and reduce image file size based on the quantisation table (QT) in JPEG image compression. To this end, we have used two general multi-objective approaches: scalarisation and Pareto-based. In this paper, we embed our strategy into five scalarisation algorithms, including energy-aware multi-objective genetic algorithm (EnMOGA), energy-aware multi-objective particle swarm optimisation (EnMOPSO), energy-aware multi-objective differential evolution (EnMODE), energy-aware multi-objective evolutionary strategy (EnMOES), and energy-aware multi-objective pattern search (EnMOPS). Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented. With our proposed scalarisation method, user's preferences can be set before starting the optimisation process and the algorithm generates only one solution based on the preference, while our Pareto-based approaches generate a set of solutions so that a user can select one of the preferred solutions after the optimisation process. Experimental studies show that the performance of the baseline algorithm is improved by embedding the proposed strategy into metaheuristic algorithms. In particular, EnMOGA, EnMOPS, and EnNSGA-II can perform competitively, among others. From the results, the baseline algorithm in all cases and in comparison to all algorithms yields the worst results. Among the scalarisation methods, EnMOGA and EnMOPS can achieve the first rank in 6 and 7 out of 13 cases and the second rank in 7 and 5 cases in terms of objective function. Also, EnMOES achieved the fifth or worst rank among the scalarisation algorithms. Regarding the Pareto-based algorithms, the table shows that EnNSGAII outperforms EnNSGAIII in 10 out of 13 cases in terms of hyper-volume measure, while it fails in 3 cases. Furthermore, we statistically verify the proposed algorithm's effectiveness based on the Wilcoxon-signed rank test. Finally, a sensitivity analysis of the parameters is provided. The source code for reproducing the results is available in: https://github.com/SeyedJalaleddinMousavirad/MultiobjectiveJPEGImageCompression. • Customer satisfaction is crucially affected by energy consumption in mobile devices. • There is a correlation between energy consumption, image quality and image file size. • We introduce a multi-objective formulation for JPEG image compression. • The proposed strategy is embedded into scalarisation and Pareto-based approaches. [ABSTRACT FROM AUTHOR]

Abandoned areas with substances potentially harmful to the environment or human health have raised numerous concerns around the world. The objective of the present study is to analyze the possibility of building solar power plants capable of capturing solar energy in unproductive areas, both contaminated and uncontaminated. For this purpose, American data from the National Solar Radiation Database (NSRDB) were used, a collection of hourly solar radiation measurements and meteorological data, as well as data from the RE-Powering America's Land project, run by the United States Environmental Protection Agency (EPA). In the analysis, the information about “mapped area”, “distances to the transmission lines”, “solar direct normal irradiance on a utility scale” and “off-grid direct normal irradiance” were considered. To define the best locations, the data were initially pre-processed. A new hybrid fuzzy c-means (HFCM) algorithm was then applied, initialized, comparatively, by three metaheuristics: Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for the data clustering. The number of clusters obtained was validated by three metrics: Calinski-Harabasz Criterion, Davies-Bouldin Criterion and Silhouette Coefficient, with all three unanimously indicating two clusters as the ideal number: one cluster for locations with greater potential for allocating facilities to capture solar energy, and another for locations with a lower potential. With the new approach, an increase of 23.3% in the training velocity of the HFCM algorithm was identified, which required fewer iterations to achieve the same value of the objective function. Else, a round of experiments was conducted with six different datasets (instances from literature) and the results showed that the proposed method can achieve better results (faster convergence and smaller solution cost) than the classic FCM. Visually, the predominance of the allocation of facilities was perceived in states with a greater average incidence of solar radiation. Therefore, this was the predominant factor in the convergence of the algorithm, which is in accordance with expectations for solar energy. Finally, the social, economic and environmental gains were considered with the revitalization of unproductive land with the possibility of implementing solar power plants in these areas. [ABSTRACT FROM AUTHOR]

In the last decades, natural gas from unconventional reservoirs has become a major portion of total gas supply due to advances in horizontal well drilling and multi-stage hydraulic fracturing as well as reduction of operational costs and capital expenditures. However, hydraulic fracturing technique is a still costly and resource intensive production strategy that requires optimal planning to conform to the best safety practices and to obtain the highest returns on investments. Thus, the proposed fast and reliable hydraulic fracture (HF) placement optimization technique based on physics and analytical equations is the key tool to balance between gas production costs and anticipated revenues. In this study, we develop an analytical model in which the modified Wattenbarger slab model with the pseudo-pressure approach are integrated into the Net Present Value (NPV) as the objective function. We consider four decision variables including number of HF stages, HF spacing, HF half-length, and wellbore spacing and use three stochastic gradient-free optimization methods (i.e., genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO)) to optimize the objective function on a synthetic shale gas reservoir model with the Barnett Shale properties. To verify the accuracy of the obtained optimal solutions, we conduct four trials for each stochastic optimization method with 100 generations and the population size of 20. The results show that the best overall value of the NPV found by PSO are 1.7% and 7.6% higher than those obtained by DE and GA, respectively. Moreover, PSO has the fastest convergence rate (in 50 generations), saves at least 10% of the computational time in comparison to those required by other methods, and results in the same optimal solution in all trials. Finally, considering bilinear flow at the early stages of the production period, nonlinear flow at the late production time, and gravitational effects in the analytical model are still open areas for future research in this field. [ABSTRACT FROM AUTHOR]

This paper presents GIS-based new ensemble data mining techniques that involve an adaptive neuro-fuzzy inference system (ANGIS) with genetic algorithm, differential evolution, and particle swarm optimization for landslide spatial modelling. This research was tested in Hanyuan County, which is a landslide-prone area in Sichuan Province, China. Different continuous and categorical landslide conditioning factors according to a literature review and data availability were selected, and their maps were digitized in a GIS environment. These layers are the slope angle, slope aspect, altitude, plan curvature, profile curvature, topographic wetness index, distance to faults, distance to rivers, distance to roads, lithology, normalized difference vegetation index and land use. According to historical information of individual landslide events, interpretation of the aerial photographs, and field surveys supported by the Sichuan Land Resources Bureau of China, 225 landslides were identified in the study area. The landslide locations were divided into two subsets, namely, training and validating (70/30), based on a random selection scheme. In this research, a probability certainty factor (PCF) model was used for the evaluation of the relationship between the landslides and conditioning factors. In the next step, three data mining techniques combined with the ANFIS model, including ANFIS-genetic algorithm (ANFIS-GA), ANFIS-differential evolution (ANFIS-DE), and ANFIS-particle swarm optimization (ANFIS-PSO), were used for the landslide spatial modelling and its zonation. Finally, the landslide susceptibility maps produced by the mentioned models were evaluated by the ROC curve. The results showed that the area under the curve (AUC) of all of the models was > 0.75. At the same time, the highest AUC value was for the ANFIS-DE model (0.844), followed by ANGIS-GA (0.821), and ANFIS-PSO (0.780). In general, the proposed ensemble data mining techniques can be applied for land use planning and management of landslide susceptibility and hazard in the study area and in other areas. [ABSTRACT FROM AUTHOR]

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. • A non-binary mutation matrix is constructed without artificial adjustable parameters. • The model incorporates gene association and balances coverage and mutual exclusivity. • A PSO algorithm suitable for solving combinatorial problems is presented. • A co-operative co-evolution algorithm is put forward for solving the presented model. • The performance is compared among the proposed method and seven state of the art ones. [ABSTRACT FROM AUTHOR]

A tremendous amount of opinionated data is being added to online platforms every day. Social media has become the primary source of collecting user's opinions about products they have purchased. These online reviews contain important information for the sellers but the information is available in unstructured natural language text. There is a dire need for an automated approach that can extract sentiment from a large number of unstructured reviews. LSTM has shown state-of-the-art results for sentiment analysis in recent years. The performance of LSTM heavily depends on the architectural design. Rather than applying a trial and error approach which can easily mislead the results, an automated optimization technique should be used for architecture related hyperparameter selection. To address this problem, we propose a new framework to optimize the architecture of LSTM using different meta-heuristics. This is the first systematic study of architectural optimization in the context of sentiment analysis using meta heuristics. We have applied Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Firefly, and Cat Swarm Optimization (CSO) for LSTM architecture optimization. The problem of training LSTM has been formulated as an optimization problem and the objective is to maximize F-score. Four benchmark datasets for sentiment analysis have been used in our experiments. PSO and DE show remarkable success in improving the F-score and accuracy. Experimental results demonstrate that the optimal configuration obtained for designing the LSTM architecture using our proposed meta-heuristics significantly improves the accuracy of sentiment analysis. • Architecture optimization of LSTM has been formulated as a single objective optimization problem. • Investigated the effect of hyper parameters for sentiment analysis using LSTM optimization. • Optimized LSTM is tested on benchmark datasets from product reviews, and Twitter. • Optimized LSTM is compared with simple LSTM, and other published works on sentiment analysis. [ABSTRACT FROM AUTHOR]

This paper proposes a joint-optimization framework for UAV-routing and UAV-route scheduling problems associated with the UAV-assisted delivery system. The mixed-integer linear programming (MILP) models for UAV-routing and UAV-route scheduling problems are proposed considering the effect of incidental processes and the varying payload on travel time. A hybrid genetic and simulated annealing (HGSA) algorithm is proposed for the UAV-routing problem to minimize travel time. In HGSA, genetic algorithm (GA) employs a novel stochastic crossover operator to search for the optimal global position of customers, whereas simulated annealing (SA) utilizes local search operators to avoid the local optima. A UAV-Oriented MinMin (UO-MinMin) algorithm is also proposed to minimize the makespan of the UAV-route scheduling problem. It employs a UAV-oriented view to generate the route-scheduling order with minimal computational efforts without affecting the quality of the makespan. A Monte Carlo simulation-based sensitivity analysis is conducted to evaluate the impact of the hybridization probability of GA and SA in the proposed HGSA algorithm. To assess the performance of the HGSA algorithm, a set P of 24 benchmark instances is adopted and adjusted to meet the constraints of the UAV-Assisted delivery system. The proposed HGSA outperforms the state-of-the-art algorithms such as genetic algorithm (GA), Particle Swarm Optimization & Simulated Annealing algorithm (PSO-SA), Differential Evolution & Simulated Annealing (DE-SA), and Harris-hawks optimization (HHO). For all 24 instances, the aerial routes generated by HGSA have been used to evaluate the effectiveness of the UO-MinMin algorithm for different numbers of UAVs. The proposed UO-MinMin algorithm outperforms the base algorithms such as minimum completion time (MCT) and opportunistic load balancing (OLB). • The proposed model supports multi-parcel delivery in a UAV-assisted delivery system. • MILP models for routing and route scheduling of UAVs are proposed. • An efficient hybrid approach is proposed for UAV-routing and UAV-route scheduling. • A new dataset is adopted to simulate the UAV-Assisted delivery system. • The hybrid approach performs better for all problem instances. [ABSTRACT FROM AUTHOR]

Highlights: [•] Fuzzy based evolutionary algorithm. [•] FACTS devices for reactive power optimization. [•] FACTS devices for minimum operating cost of a power system. [Copyright &y& Elsevier]

Abstract: Evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels. [Copyright &y& Elsevier]

Abstract: This paper proposes a penalty based differential evolution (P-DE) for extracting the parameters of solar photovoltaic (PV) modules at different environmental conditions. The two diode model of a solar cell is used as the basis for the extraction problem. The analyses carried out using synthetic current–voltage (I–V) data set showed that the proposed P-DE outperforms other Evolutionary Algorithm methods, namely the simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO). P-DE consistently converges to the global optimum values very rapidly. The performances are evaluated using the well known quality test and student T-tests. Furthermore, the P-DE extraction method is practically validated by six solar modules of different types (multi-crystalline, mono-crystalline, and thin-film). The results were found to be in close agreement with the experimental I–V data set, especially at very low irradiance values. The latter can be very useful in predicting the performance of the solar system under partial shading conditions. The main application of the proposed work is the possibility of developing a highly accurate simulator for solar PV system designer. [Copyright &y& Elsevier]

Abstract: This paper presents new multi-objective evolutionary hybrid algorithms for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithms are memetic Pareto particle swarm optimization based RBFN (MPPSON), Memetic Elitist Pareto non dominated sorting genetic algorithm based RBFN (MEPGAN) and Memetic Elitist Pareto non dominated sorting differential evolution based RBFN (MEPDEN). The proposed methods integrate accuracy and structure of RBFN simultaneously. These algorithms are implemented on two-class and multiclass pattern classification problems with one complex real problem. The results reveal that the proposed methods are viable, and provide an effective means to solve multi-objective RBFNs with good generalization ability and simple network structure. The accuracy and complexity of the network obtained by the proposed algorithms are compared through statistical tests. This study shows that the proposed methods obtain RBFNs with an appropriate balance between accuracy and simplicity. [Copyright &y& Elsevier]

Abstract: Due to its ability to handle nonlinear functions regardless of the derivatives information, evolutionary algorithms (EA) are envisaged to be very effective for extracting parameter of photovoltaic (PV) cell. This paper presents critical evaluation of the parameters extraction of two diode PV model using three EA methods, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE). For DE, two variations are proposed: (1) boundary based differential evolution (B-DE) and (2) penalty based differential evolution (P-DE). The performance of each method is evaluated based on several factors: accuracy and consistency of solution; speed of convergence; computational efficiency and the required number of control parameters. Comparisons are carried out using synthetic data and are validated by six PV modules of different types (multi-crystalline, mono-crystalline, and thin-film) from various manufacturers. Information derived from these critical evaluations can be useful to determine the best computational method to build an efficient and accurate PV system simulator. [Copyright &y& Elsevier]

Abstract: The graph partitioning is usually tackled as a single-objective optimization problem. Moreover, various problem-specific versions of different algorithms are proposed for solving this integer-valued problem, thus confusing practitioners in selecting an effective algorithm for their instances. On the other hand, although various metaheuristics are currently in great consideration towards different problem-domains, these are yet to be investigated widely to this problem. In this article, a novel attempt is made to investigate whether some common and established metaheuristics can directly be applied to different search spaces, instead of going through various problem-specific algorithms. For this, some mechanisms are proposed for handling the graph partitioning problem by general multi-objective real-valued genetic algorithm, differential evolution, and particle swarm optimization. Some algorithmic modifications are also proposed for improving the performances of the metaheuristics. Finally, the performances of the metaheuristics are compared in terms of their computer memory requirements, as well as their computational runtime and solution qualities based on some test cases with up to five objectives. [Copyright &y& Elsevier]

Abstract: Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments. [Copyright &y& Elsevier]

Abstract: The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly. [Copyright &y& Elsevier]

Abstract: Parameter estimation for vapor–liquid equilibrium (VLE) data modeling plays an important role in design, optimization and control of separation units. This optimization problem is very challenging due to the high non-linearity of thermodynamic models. Recently, several stochastic optimization methods such as Differential Evolution with Tabu List (DETL) and Particle Swarm Optimization (PSO) have evolved as alternative and reliable strategies for solving global optimization problems including parameter estimation in thermodynamic models. However, these methods have not been applied and compared with respect to other stochastic strategies such as Simulated Annealing (SA), Differential Evolution (DE) and Genetic Algorithm (GA) in the context of parameter estimation for VLE data modeling. Therefore, in this study several stochastic optimization methods are applied to solve parameter estimation problems for VLE modeling using both the classical least squares and maximum likelihood approaches. Specifically, we have tested and compared the reliability and efficiency of SA, GA, DE, DETL and PSO for modeling several binary VLE data using local composition models. These methods were also tested on benchmark problems for global optimization. Our results show that the effectiveness of these stochastic methods varies significantly between the different tested problems and also depends on the stopping criterion especially for SA, GA and PSO. Overall, DE and DETL have better performance for solving the parameter estimation problems in VLE data modeling. [Copyright &y& Elsevier]

• A general algorithm framework for hardware software partitioning problem is proposed based on evolutionary algorithms. • An effective method to deal with the infeasible solution of hardware software partitioning problem is proposed • The performances of genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE), and group theory based optimization algorithm (GTOA) for solving hardware software partiti on ing problem are compared. • According to the comparison results, it is pointed out that GTOA and BPSO are more suitable for solving hardware software partitioning problem than GA and HBDE Hardware/software partitioning (HW/SW) is a significant problem in hardware-software co-design, and it is also an NP-hard problem. In order to solve the HW/SW quickly and effectively by evolutionary algorithms, the HW/SW is firstly regarded as a variant of knapsack problem. Based on a new greedy strategy, a greedy repair and optimization algorithm GROM is proposed to eliminate the infeasible solutions. Subsequently, a general algorithm framework based on discrete evolutionary algorithm for HW/SW problem is proposed. On the basis of the above algorithm framework, genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE) and group theory-based optimization algorithm (GTOA) are used to solve large-scale HW/SW instances. The feasibility and effectiveness of the algorithm framework proposed in the paper are verified by comparing the good and bad of the calculation results of above algorithms, and pointed out that the performance of GTOA and BPSO is better than that of HBDE and GA, they are more suitable for solving large-scale HW/SW problem. [ABSTRACT FROM AUTHOR]

In recent years, bio-inspired optimization algorithms have demonstrated the ability to produce optimal solutions to numerous complex computational problems in science and engineering. In this work, a comparative analysis of bio-inspired algorithms is presented to understand and quantify the performance of algorithms in guiding the search process toward better solutions over all feasible solutions. Three evolutionary algorithms, namely, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), are implemented to generate an optimized test sequence set for digital sequential circuits to investigate how they explore and the exploitation realized in their search spaces. The merit of using a comparative method to analyze and optimize search spaces is to reach a reliable and quantifiable conclusion about the relative performance of each algorithm. An improvement in the quality of solutions was achieved, particularly in terms of testing time, number of test vectors, and fault coverage of tested sequential circuits in comparison with other algorithmic test generators that have been presented in the literature. The study shows how to effectively reduce the search space without negatively affecting the results and guides the search process over a large space. In addition, the experiments highlight the limitations of each optimization algorithm and offer some constructive methods of improvement. Moreover, several recommendations and guidelines regarding the use of optimization algorithms as test pattern generators to improve their performance and increase their efficiency are presented. Finally, we emphasize the relevance of bio-inspired algorithms in solving complex computational problems, data manipulation, and optimization objectives. • A comparative analysis of bio-inspired algorithms to evaluate their performance in searching for optimum solutions. • GA, DE and PSO are studied to generate test sequence sets for sequential circuits. • Parameters of each algorithm have different impacts on the searching mechanism. • PSO shows a significant capability over other algorithms to find optimal solutions. [ABSTRACT FROM AUTHOR]

Compact optimization is an alternative paradigm in the field of metaheuristics requiring a modest use of memory to optimize a problem. As opposed to population-based algorithms, which conduct the search by employing a set of candidate solutions, compact algorithms use a probabilistic model to describe how solutions are distributed over the search space. Compared to other Estimation of Distribution Algorithms, peculiar features such as the use of simple probabilistic models, in which variables are treated independently, and the need for a minimal number of solutions to be sampled to perform the search, make these algorithms suitable for those applications plagued by memory limitations. Compact algorithms show good results on different kinds of optimization problems but often prematurely converge and perform poorly on non-separable. In this paper, we attempt to overcome these limitations by combining compact algorithms with a restart mechanism named Re-Sampled Inheritance (RI) whose purpose is to avoid premature convergence while also inheriting parts of the variables from the best solution found so far. To assess the effect of the RI mechanism, we extensively test various existing compact algorithms, with and without RI, and compare the best RI-based compact algorithm against several competing algorithms on several optimization problems at different dimensionalities. We also evaluate the effect of the RI parameters on the overall algorithmic performance. Our numerical results not only show that RI consistently enhances the performances of compact algorithms, but also shed some light on the effectiveness of different compact logics at handling problems at different dimensionalities. • We present a framework combining a partial restart mechanism with compact algorithms. • We test the framework on a broad range of benchmark and real-world problems. • Different memory-limited algorithms perform differently at different scales. • Compact algorithms with restart can perform better than population-based algorithms. [ABSTRACT FROM AUTHOR]

• A new method has been proposed, known as MSCA, for global optimization. • The MSCA improves the SCA using a novel transition parameter and mutation operator. • A set of 33 benchmark problems is used to examine the MSCA. • The MSCA is also used to solve real-engineering problems and to train multilayer perceptron. • Comparisons illustrate the improvement in the performance of the MSCA. Inspired by the mathematical characteristics of sine and cosine trigonometric functions, the Sine Cosine Algorithm (SCA) has shown competitive performance among other meta-heuristic algorithms. However, despite its sufficient global search ability, its low exploitation ability and immature balance between exploitation and exploration remain weaknesses. In order to improve Sine Cosine Algorithm (SCA), this paper presents a modified version of the SCA called MSCA. Firstly, a non-linear transition rule is introduced instead of a linear transition to provide comparatively better transition from the exploration to exploitation. Secondly, the classical search equation of the SCA is modified by introducing the leading guidance based on the elite candidate solution. When the above proposed modified search mechanism fails to provide a better solution, in addition, a mutation operator is used to generate a new position to avoid the situation of getting trapped in locally optimal solutions during the search. Thus, the MSCA effectively maximizes the advantages of proposed strategies in maintaining a comparatively better balance of exploration and exploitation as compared to the classical SCA. The validity of the MSCA is tested on a set of 33 benchmark optimization problems and employed for training multilayer perceptrons. The numerical results and comparisons among several algorithms show the enhanced search efficiency of the MSCA. [ABSTRACT FROM AUTHOR]