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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]

In this paper, a new hybrid YUKI-ANN is implemented for Structural Health Monitoring (SHM) of laminated composite plates. A finite element model is constructed and used to identify damage at five randomly selected elements in a laminated composite plate. The process of damage identification is divided into two main steps, namely damage localization and quantification. During the localization step, damaged elements are identified using a damage indicator based on Modal Strain Energy change ratio (MSEcr). After excluding the healthy elements, the level of damage is predicted using ANN modified with four optimization algorithms: Arithmetic Optimizing Algorithm (AOA), Balancing Composite Motion Optimization (BCMO), Particle Swarm Optimization (PSO), and YUKI algorithm. The performance of these optimization algorithms is evaluated, and it is found that the YUKI algorithm (YA) outperforms AOA, BCMO, and PSO algorithms without exception. YA gives better predictions with lower errors when compared to PSO and BCMO algorithms with equivalent computational time. In some cases, AOA provides slightly better predictions than YA, but the computational time for these predictions is eight times more that of YA. If both performance and efficiency are considered, YA seems to be the best choice. Find the MATLAB code for YUKI-ANN at https://github.com/Brahim-Benaissa/YUKI_ANN. [ABSTRACT FROM AUTHOR]

In the area of association rule mining, many optimization algorithms have been proposed to improve the computational efficiency of rule mining or the quality and diversity of association rules. However, in real applications, since the user may have prior knowledge and research trends for some key items, the association rules containing key items are more valuable and meaningful for these users. This contributes to a new issue that association rules related to key items should be mined in a targeted manner. To solve this issue, this paper proposes a novel animal dynamic migration optimization (ADMO) method to realize directional rule mining as well as maintain high mining efficiency and high rule quality. Taking the support and confidence of frequent itemsets as input, the method first identifies valuable rules and then initializes and updates the animal population to search for the best animal. The support and confidence of the best animal are defined as threshold values to delete unnecessary rules and discover more key rules. During the optimization, the population size value is dynamically generated. The effectiveness and applicability of ADMO are validated on 11 open-source datasets and a real-world elevator case. The results reveal that the ADMO method has a faster mining speed and obtains more key rules than the ARM-PSO, ARM-AMO, ARM-MOPSO, ARM-WOA, and ARM-DE methods. In the elevator case, the association rule generated by ADMO can provide a higher success rate and accuracy for requirement transformation. • A heuristic optimization method is proposed to mine association rules directionally. • Valuable rules can be identified in advance to reduce computational consumption. • The population size value is dynamically generated without manually setting. • The proposed method has been tested on 11 real datasets and a real case. • The proposed method has been compared with the latest methods. [ABSTRACT FROM AUTHOR]

This paper presents the dimensional synthesis of four-bar mechanisms for path generation. The synthesis of the four-bar mechanisms is proposed using nine advanced optimization algorithms, namely the Rao-1 algorithm, Rao-2 algorithm, Rao-3 algorithm, self-adaptive multi-population (SAMP) Rao-1 algorithm, SAMP Rao-2 algorithm, SAMP Rao-3 algorithm, quasi-oppositional-based (QO) Rao-1 algorithm, QO Rao-2 algorithm, and QO Rao-3 algorithm. Six different cases of synthesis of a four-bar mechanism are considered. The brief literature review based on the synthesis of mechanisms is also presented. The designs of mechanisms obtained using Rao algorithms and their variants are compared with the designs acquired using other advanced optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, differential evolution (DE), imperialist competitive algorithm (ICA), hybrid GA-DE algorithm, simulated annealing (SA), Malaga university mechanism synthesis algorithm (MUMSA), IMMa optimization algorithm (IOA), hybrid Taguchi random coordinate search algorithm (HTRCA), hybrid GA and fuzzy logic (GA–FL) algorithm, ant-gradient method (AGM), teaching–learning-based optimization algorithm (TLBO), and hybrid Lagrange interpolation differential evolution algorithm (HLIDE). The comparison of results reveals the efficacy of Rao algorithms and their variants for synthesizing the four-bar mechanism. [Display omitted] • Dimensional synthesis of four-bar mechanisms for path generation is presented. • The synthesis is carried out using Rao algorithms and their variants. • The designs obtained are compared with those obtained by the other algorithms. • The comparison of results reveals the effectiveness of the Rao algorithms and their variants. • The Rao algorithms and their variants are easy to implement. [ABSTRACT FROM AUTHOR]

In the paper a modification of a Socio-cognitive Particle Swarm Optimization algorithm, recently proposed by the authors, is presented. This modification consists in devising a mechanism for dynamic adaptation of the population structure of the swarm. Besides the design and rationale for the approach, referring to the state-of-the-art PSO original algorithm and several of its modifications, experimental results tackling popular benchmark functions are presented and discussed in detail. [ABSTRACT FROM AUTHOR]

This paper proposes a new Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems especially under Partial Shading (PS). This technique is based on Generalized Pattern Search (GPS) optimization algorithm. The proposed technique adopts the GPS algorithm owing to the reason that its major features like simple implementation, derivative free nature, fast convergence and high accuracy are ideally suited to deal with PS conditions. Furthermore, these features of GPS can well resolve the limitations and drawbacks of previous MPPT techniques, such as fluctuation, low efficiency, high complexity and slow convergence. For comparison, our proposed MPPT algorithm, the Particle Swarm Optimization (PSO) algorithm and the Perturb and Observe (P&O) algorithm have been implemented in MATLAB/Simulink. The comparison results demonstrate that the proposed MPPT technique can efficiently detect the global maximum (GM) in a fewer number of voltage samples, which is nearly 4 times less than the number of voltage samples required by PSO. Moreover, the steady state efficiency of the proposed MPPT is 99.8%, which is higher than the other MPPT techniques. Therefore, the proposed technique distinguishes itself from other MPPT techniques due to its superior quality in terms of convergence speed, dynamic and steady state efficiencies and low complexity. [ABSTRACT FROM AUTHOR]

Different alarm thresholds could generate different alarm data, resulting in different correlations. A new multivariate alarm thresholds optimization methodology based on the correlation consistency between process data and alarm data is proposed in this paper. The interpretative structural modeling is adopted to select the key variables. For the key variables, the correlation coefficients of process data are calculated by the Pearson correlation analysis, while the correlation coefficients of alarm data are calculated by kernel density estimation. To ensure the correlation consistency, the objective function is established as the sum of the absolute differences between these two types of correlations. The optimal thresholds are obtained using particle swarm optimization algorithm. Case study of Tennessee Eastman process is given to demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid optimizer in which an innovative optimal particles local search strategy on basis of bound optimization by quadratic approximation (BOBYQA) algorithm and exterior penalty function method is integrated into particle swarm optimization (PSO). The main goal of the approach is to improve the convergence performance of PSO, and preserve the diversity of non-dominated set. Our algorithm selects some non-dominated solutions lied in less-crowded region of external archive based upon crowding distance value to construct a leader particles set, and make full use of optimal particles method to guide leader particles approach the Pareto front quickly. Meanwhile, a local optimal particles search strategy is proposed after particular analysis on disadvantage of global optimal particle search method, and names our algorithm as LOPMOPSO. Furthermore, the multi-dimensional uniform mutation operator is performed to prevent algorithm from trapping into local optimum, and a dynamic archive maintenance strategy is applied to improve the diversity of solutions. For coping with the constrained conditions consists in objective functions, we adopt an efficient infeasibility degree evaluation criterion to deal with these complex problems. Simulation results of various kinds of benchmark functions show that our approach is highly competitive in convergence speed and generates a well distributed and accurate set of non-dominated solutions easily. The solving of a 2-D aerodynamic optimization problem further validates its speed and effectiveness. [ABSTRACT FROM AUTHOR]

With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource allocation of joint time slot assignment, channel allocation and power control for MRMC WSNs. Due to the diversity of research objectives and the computational complexity of the non-convex problem, this paper develops a two-stage resource allocation optimization algorithm by analyzing the interdependence of various resources. Specifically, to exchange information with conflict-free transmission among all sensors, a graph coloring algorithm for time slot assignment is designed firstly. Then based on the first stage of this algorithm, the problem of joint power control and channel allocation is studied and formulated as a multi-objective optimization problem to achieve the trade-off between energy efficiency and network capacity maximization under the constraints of link interference and load balance. Multi-objective hybrid particle swarm optimization is introduced to obtain the Pareto optimal solutions. The simulation results show that the proposed algorithm significantly performs better in terms of achieving the trade-off of multi-performance. • Interference and network capacity models are constructed. • Reveal the strong coupling of power, channel, and time slot for MRMC WSN. • A graph coloring algorithm is designed to achieve conflict-free transmission. • Design a multi-objective hybrid particle swarm optimization with low complexity. • Particle swarm optimization can obtain the Pareto optimal solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel non-dominated sorting genetic algorithm-based method for scheduling simultaneously different types of vehicles with different capacities in distribution centers working as cross dock. In this paper, a three-objective model is proposed, which minimizes operational time, lateness and earliness of delivering products. In many of real life cases, packs of products with different number of products must be unloaded instead of arbitrary number of products because of products' nature and/or physical limitations. It means that the optional amount of unloading is not prohibited in many of real life cases unlike to previous researches; therefore, scheduling vehicles by considering this limitation is different from previous works that allow optional amount of unloading. Another advantage of this paper is consideration of the frequent unloading pattern of inbound vehicles. This consideration better synchronizes the inbound and outbound vehicles compared to non-frequent pattern, and as a result, it helps to reduce the operational time of cross docking as well as the shipping cycle. This paper proposes a new scheduling method for considering the mentioned novelties. Furthermore, Taguchi design is used for regulating the proposed algorithm's parameters. Several numerical examples are solved by the proposed method, and the obtained results are compared to multi-objective particle swarm optimization. The numerical results show the superiority of the proposed method. • A novel algorithm for scheduling different types of vehicles in cross docking systems. • A three-objective model, which minimizes operational time, lateness and earliness. • Considering Packed shipment scenario unlike previous works. • Considering the frequent unloading pattern of inbound vehicles. [ABSTRACT FROM AUTHOR]

The task assignment problem (TAP) is one of the standard combinatorial optimization problems (COPs) in the field of discrete optimization. TAP is known to be an NP-complete problem due to the difficulty to obtain the exact solution of it in polynomial time. Thus, it is essential to develop and/or propose an optimization algorithm to solve this problem. In TAPs, a set of tasks is assigned to a set of machines to both effectively minimize the communication and execution cost. This paper presents a modified Salp Swarm Algorithm (SSA), to solve not only task assignment problems but also, fundamental combinatorial optimization problems in engineering and real-world scientific domains. The modified salp algorithm takes place in the integration with the Local Refinement Heuristic (LRH) approach to enhance a given assignment along with operators. The modified algorithm is named Modified Salp Local Refinement Heuristic (MSLRH). To the best of our knowledge, this paper is the first of its kind to attempt using the SSA in task assignment problems. The MSLRH algorithm is tested on different benchmark datasets (tree structure and general graph), including various tasks and machines for each dataset. In addition, it compared with the most known meta-heuristic algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, and Jaya algorithm (JAYA) to investigate the effectiveness of the MSLRH algorithm in terms of average assignment allocation cost. From tree structure dataset (e.g., 200 tasks assigned to 8 machines), the proposed MSLRH algorithm has achieved a minimum average assignment cost better than GA by 62% and better than PSO and JAYA by 42%. From the general graph dataset (e.g., 209 tasks assigned to 16 machines), the proposed MSLRH algorithm has better results than other algorithms up to 60%. From various and extensive experimental results, the proposed algorithm has proven the effectiveness in solving the task assignment problem. • A modified version of Salp Swarm Algorithm (SSA) to solve task assignment problems (TAPs) in a discrete domain is proposed. • The proposed algorithm is tested on two sets of problem instances with different characteristics of task/machine heterogeneity. • The efficiency of the proposed algorithm is investigated by comparing it with three meta-heuristic algorithms. • Extensive analysis of the proposed algorithm to show its robustness is presented. [ABSTRACT FROM AUTHOR]

• Logarithmic spiral path enhances search agents' exploitation. • The exploration rate is made dynamic to improve the convergence. • The new procedure is tested by minimizing the 29 well known functions. • The proposed optimizer is validated using the 6 real cases. Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA's search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a Levy distribution instead of the traditional uniform one. However, due to its poor exploitation in local areas, the LF-FA does not guarantee fast convergence. To address this problem, this paper provides an adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the LF-FA's local exploitation and accelerates its convergence. Our AD-IFA is integrated with logarithmic-spiral guidance to its fireflies' paths, and adaptive switching between exploration and exploitation modes during the search process. Experimental results show that the AD-IFA presented in this paper consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum. [ABSTRACT FROM AUTHOR]

Target searching with autonomous robots require an efficient target search method that considers their constraints and environmental characteristics. Particle swarm optimization (PSO) is a fantastic population-based optimization algorithm. It is often used in swarm robotics cooperative search jobs because of its inspiration resources and velocity updating function. Given the global optimization features of PSO, it is simple to converge on a particular location in a search environment and miss out on opportunities to learn more. This paper proposes an adaptive exploration robotic PSO (AERPSO) to solve multi-target search problems. The proposed method enhances the chances of exploring unexplored regions and helps with obstacle avoidance using evolutionary speed and aggregation degree. The adaptive inertia weight helps in enhanced exploration. The simulation results compiled from various simulation experiments show that AERPSO performs way ahead of the existing state-of-the-art techniques for target searching. The proposed algorithm improves the search time by approximately 40% and the detection rate by 25% in comparison with existing approaches. It balances exploration and exploitation to become an excellent approach for multi-target searching. • To propose an adaptive exploration robotic PSO to search multiple targets. • The method avoids local minima and keeps the diversity among the population. • It enhances the exploration by adapting the inertia weights. • The proposed algorithm deals with overshooting problem of PSO. [ABSTRACT FROM AUTHOR]

• The path planning and path smoothing are combined through the high-order continuous Bezier curve. • An improved QPSO that incorporates two search mechanisms from bald eagle search (BES) is proposed. • The concept of semi-randomization is infused in some of our modifications in BES-MQPSO. • Enhanced intensive exploitation is performed by incorporating BES search and swoop mechanisms. • It may be the first time to apply BES tricks to help to enhance QPSO and into the path planning field. The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively. [ABSTRACT FROM AUTHOR]

• A new distance domination relation is proposed to select the non-dominant solution. • The association criterion of NSGA- III algorithm is improved. • Combined with the proposed distance domination relation improved NSGA- III algorithm. There are two main aspects of research in many-objective optimization algorithm, namely, convergence and diversity. However, it is difficult for original algorithms to maintain the diversity of solutions in the high-dimensional objective space. The NSGA-III algorithm is an advanced algorithm based on Pareto dominance. In the high-dimensional objective space, the diversity maintenance of this algorithm is obviously lacking. In order to enhance the diversity of algorithms in many-objective optimization problems, a new distance dominance relation is proposed in this paper. First, in order to ensure the convergence of the algorithm, the distance dominance relation calculates the distance from the candidate solution to the ideal point as the fitness value, and selects the candidate solution with good fitness value as the non-dominant solution. Then, in order to enhance the diversity of the algorithm, the distance dominance relation sets each candidate solution to have the same niche and ensures that only one optimal solution is retained in the same territory. Finally, the NSGA- III algorithm is improved based on the proposed distance dominance relation. On the DTLZ and MaF test problems with 3, 5, 8, 10, and 15 objectives, the improved algorithm is compared with seven commonly used algorithms. The experimental results show that the improved algorithm is highly competitive and can significantly enhance the diversity of the algorithm. [ABSTRACT FROM AUTHOR]

Intelligent optimization algorithms are widely utilized to deal with complex optimization problems in various areas. However, a single intelligent optimization algorithm cannot handle well more and more complex optimization problems. The ensemble strategy can integrate several different operators and algorithms by using some appropriate strategies and maybe obtain better optimization performance. In this paper, a new ensemble multi-swarm method based on teaching–learning-based optimization (EMTLBO) was proposed by integrating three different algorithms including the original teaching–learning-based optimization algorithm, its variant with neighborhood search and the variant with differential evolution. In EMTLBO, a new evaluating mechanism based on the fitness-based and diversity-based metrics (FDEM) for each sub-swarm was proposed to evaluate the optimization performance after a continuous generation interval. Moreover, an algorithm matching mechanism based on ranking for sub-swarms (AMM) is adapted to re-divide the population into three sub-swarms and match a suitable algorithm for each sub-swarm so as to increase the whole optimization performance. Furthermore, the experimental results on CEC2014 and CEC2017 test suits verify the feasibility and optimization performance of EMTLBO. Finally, the proposed algorithm is extended to optimize the segmentation thresholds of images and the segmentation performances on different benchmark images show that EMTLBO has good performance in most cases. • A new evaluating mechanism was proposed to evaluate the optimization performance. • Algorithm matching mechanism is adopted to match a suitable algorithm for each sub-swarm. • The experimental results on CEC2014 and CEC2017 test suits verify EMTLBO. • The proposed EMTLBO algorithm is extended to deal with image segmentation problems. [ABSTRACT FROM AUTHOR]

Unmanned combat aerial vehicle (UCAV) technology has to address many challenges in complex battlefield environments. To produce a safe and low-energy flying path, a UCAV requires many points to build a path that avoids threats, which increases the problem dimension, consumes more computational resources and makes the results unstable. To address this issue, this paper employs a new model known as double-layer coding (DLC) for path planning with unevenly distributed points to decrease the number of superfluous points on the path. Meanwhile, the RPSO algorithm, which introduces a novel strategy of rotating particles in high-dimensional space to search for targets, is proposed as an enhanced particle swarm optimization (PSO) algorithm. The proposed method effectively improves PSO ′ s exploration capacity. Furthermore, RPSO is employed to implement the double-layer coding model for path planning (DLCRPSO). The experimental results show that the proposed DLCRPSO method for path planning always produces feasible flight paths in complex environments. [Display omitted] • A double-layer coding model for path planning is established. • An improved PSO with a novel exploration strategy is proposed. • The superiority of the double-layer coding model used improved PSO is verified. [ABSTRACT FROM AUTHOR]

• A generalized decoupling metaheuristic RBDO framework is proposed. • A particle's memory saving strategy is developed to provide effective guidance. • A new adaptive fractional-order equilibrium optimizer algorithm is developed. • Numerical and engineering examples illustrate the superiority of the proposed algorithm. Reliability-based design optimization (RBDO) algorithm is to minimize the objective under the probabilistic factors. While gradient-based and classical evolutionary RBDO algorithms provide promising performance on simple optimization problems, they are likely to perform poorly on challenging problems, including the multimodal functions, discrete design spaces, non-differential problems, etc. This paper proposes a unified framework to improve the performance of existing RBDO algorithms for complex RBDO problems. Our framework is based on three new strategies: generalized decoupling evolutionary and metaheuristic RBDO framework, particle's memory saving strategy, and adaptive fractional-order equilibrium optimizer algorithm. The proposed algorithm is characterized by a decoupling strategy to enable the parallel operation of the inner reliability computation and outer deterministic optimization, a particle's memory saving strategy to provide effective guidance from the previous iteration, and the adaptive fractional-order equilibrium optimizer algorithm to enhance the search efficiency and global convergence capacity. To evaluate the performance of the proposed algorithm, a wide range of experiments are conducted on different types of use cases. The experimental results demonstrate that our algorithm provides superior performance over other comparative algorithms. [ABSTRACT FROM AUTHOR]

• A novel method to improve the performance of population-based optimizers. • The combination lock algorithm used as a pre-processing optimization method. • The combination lock algorithm produces an elite individual. • The elite individual is inserted into the initial population of optimizers. • The new algorithm significantly improves the performance of optimizers. Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems. In this paper, a novel systematic pre-processing approach, called the combination lock algorithm, for obtaining a good starting point for population-based algorithms is proposed. The proposed algorithm is tested on 32 benchmark unconstrained multidimensional optimization problems of different characteristics that are either unimodal or multimodal, continuous or non-continuous, separable or non-separable, differentiable or non-differentiable. The tests also include four engineering constrained optimization benchmark problems. The experimental results of applying the proposed algorithm for the Particle Swarm Optimization, the Ant Colony Optimization for Continuous Domain, and the Grey Wolf Optimization were compared with the results obtained from the conventional approach of initializing the starting population of population-based metaheuristic methods. The simulation results show the potential of the proposed algorithm as an efficient and reliable approach to enhance the performance of population-based optimization algorithms such that, overall, 50% and up to 100% of the tested problems "across various population size" settings, had either improved or equalled the optimal values when the proposed algorithm was applied. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the ability of two new nature-inspired metaheuristics namely the flower pollination (FP) and the social spiders optimization (SSO) algorithms to solve the image segmentation problem via multilevel thresholding. The FP algorithm is inspired from the biological process of flower pollination. It relies on two basic mechanisms to generate new solutions. The first one is the global pollination modeled in terms of a Levy distribution while the second one is the local pollination that is based on random selection of local solutions. For its part, the SSO algorithm mimics different natural cooperative behaviors of a spider colony. It considers male and female search agents subject to different evolutionary operators. In the two proposed algorithms, candidate solutions are firstly generated using the image histogram. Then, they are evolved according to the dynamics of their corresponding operators. During the optimization process, solutions are evaluated using the between-class variance or Kapur's method. The performance of each of the two proposed approaches has been assessed using a variety of benchmark images and compared against two other nature inspired algorithms from the literature namely PSO and BAT algorithms. Results have been analyzed both qualitatively and quantitatively based on the fitness values of obtained best solutions and two popular performance measures namely PSNR and SSIM indices as well. Experimental results have shown that both SSO and FP algorithms outperform PSO and BAT algorithms while exhibiting equal performance for small numbers of thresholds. For large numbers of thresholds, it was observed that the performance of FP algorithm decreases as it is often trapped in local minima. In contrary, the SSO algorithm provides a good balance between exploration and exploitation and has shown to be the most efficient and the most stable for all images even with the increase of the threshold number. These promising results suggest that the SSO algorithm can be effectively considered as an attractive alternative for the multilevel image thresholding problem. [ABSTRACT FROM AUTHOR]

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design problems. This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building (nZEB) where more than 1.6 10 solutions would be possible. The compared algorithms include a controlled non-dominated sorting genetic algorithm with a passive archive (pNSGA-II), a multi-objective particle swarm optimization (MOPSO), a two-phase optimization using the genetic algorithm (PR_GA), an elitist non-dominated sorting evolution strategy (ENSES), a multi-objective evolutionary algorithm based on the concept of epsilon dominance (evMOGA), a multi-objective differential evolution algorithm (spMODE-II), and a multi-objective dragonfly algorithm (MODA). Several criteria was used to compare performance of these algorithms. In most cases, the quality of the obtained solutions was improved when the number of generations was increased. The optimization results of running each algorithm 20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity, followed by the pNSGA-II, evMOGA and spMODE-II. Uncompetitive results were achieved by the ENSES, MOPSO and MODA in most running cases. The study also found that 1400–1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. [ABSTRACT FROM AUTHOR]

This paper presents an optimal Proportional Integral Derivate (PID) controller design for an Automatic Voltage Regulator (AVR) system using a new hybrid devised from the Particle Swarm Optimization and the Gravitational Search Algorithm (PSOGSA). The transient response analysis and bode analysis were considered to show the effectiveness of the design technique. Moreover, the comparison of the results between the proposed approach and other techniques such as the Ziegler-Nichols (ZN) tuning method, the Particle Swarm Optimization (PSO) tuning method and the Many Optimizing Liaisons (MOL) tuning method have been given. According to the analysis, the proposed PSOGSA algorithm gives better results than other techniques for the AVR system. [ABSTRACT FROM AUTHOR]

Spider Monkey Optimization (SMO) technique is most recent member in the family of swarm optimization algorithms.SMO algorithm fall in class of Nature Inspired Algorithm (NIA). SMO algorithm is good in exploration and exploitation of local search space and it is well balanced algorithm most of the times. This paper presents a new strategy to update position of solution during local leader phase using fitness of individuals. The proposed algorithm is named as Fitness based Position Update in SMO (FPSMO) algorithm as it updates position of individuals based on their fitness. The anticipated strategy enhances the rate of convergence. The planned FPSMO approach tested over nineteen benchmark functions and for one real world problem so as to establish superiority of it over basic SMO algorithm. [ABSTRACT FROM AUTHOR]

In course of the new development in construction materials, micro and nanoelectromechanical systems, a new sophisticated class of machinery are rising. This has created the need of diagnostic and inspection algorithm matching with the level of artificial intelligence implemented in the end product. In this paper, we present a case study, a simulation on inspecting airplane wings using unmanned quadrotor. The aim is to automate the inspection process, maximize the efficient allocation of resources and minimize possible risks caused by human errors. In order to achieve wing inspection, a laser setup is mounted on the quadrotor, which trajectory and flight stabilization is controlled by optimized fuzzy logic position controllers using particle swarm optimization. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) and artificial bee colony (ABC) are new optimization methods that have attracted increasing research interests because of its simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optimal because of its low global exploration efficiency; ABC algorithm has slower convergence speed in some cases because of the lack of powerful local exploitation capacity. In this paper, we propose a hybrid algorithm called PS–ABC, which combines the local search phase in PSO with two global search phases in ABC for the global optimum. In the iteration process, the algorithm examines the aging degree of pbest for each individual to decide which type of search phase (PSO phase, onlooker bee phase, and modified scout bee phase) to adopt. The proposed PS–ABC algorithm is validated on 13 high-dimensional benchmark functions from the IEEE-CEC 2014 competition problems, and it is compared with ABC, PSO, HPA, ABC–PS and OXDE algorithms. Results show that the PS–ABC algorithm is an efficient, fast converging and robust optimization method for solving high-dimensional optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, a new version of the particle swarm optimization (PSO) algorithm suitable for discrete optimization problems is presented and applied for the solution of the capacitated location routing problem and for the solution of a new formulation of the location routing problem with stochastic demands. The proposed algorithm combines three different topologies which are incorporated in a constriction particle swarm optimization algorithm and, thus, a very effective new algorithm, the global and local combinatorial expanding neighborhood topology particle swarm optimization, was developed. The algorithm was tested, initially, in the three classic sets of benchmark instances for the capacitated location routing problem with discrete demands and, then, as there are no benchmark instances for the location routing problem with stochastic demands, these instances were transformed appropriately in order to be suitable for the problem with stochastic demands. The algorithm was tested in the problem with the stochastic demands using these transformed sets of benchmark instances. The algorithm was compared with a number of different implementations of the PSO and with metaheuristic, evolutionary and nature inspired algorithms from the literature for the location routing problem with discrete and stochastic demands. [ABSTRACT FROM AUTHOR]

Population-based optimization algorithms have been successfully applied to hydrological forecasting recently owing to their powerful ability of global optimization. This paper investigates three algorithms, i.e. differential evolution (DE), artificial bee colony (ABC) and ant colony optimization (ACO), to determine the optimal one for forecasting downstream river flow. A hybrid neural network (HNN) model, which incorporates fuzzy pattern-recognition and a continuity equation into the artificial neural network, is proposed to forecast downstream river flow based on upstream river flows and areal precipitation. The optimization algorithm is employed to determine the premise parameters of the HNN model. Daily data from the Altamaha River basin of Georgia is applied in the forecasting analysis. Discussions on the forecasting performances, convergence speed and stability of various algorithms are presented. For completeness׳ sake, particle swarm optimization (PSO) is included as a benchmark case for the comparison of forecasting performances. Results show that the DE algorithm attains the best performance in generalization and forecasting. The forecasting accuracy of the DE algorithm is comparable to that of the PSO, and yet presents weak superiority over the ABC and ACO. The Diebold–Mariano (DM) test indicates that each pair of algorithms has no difference under the null hypothesis of equal forecasting accuracy. The DE and ACO algorithms are both favorable for searching parameters of the HNN model, including the recession coefficient and initial storage. Further analysis reveals the drawback of slow convergence and time-consumption of the ABC algorithm. The three algorithms present stability and reliability with respect to their control parameters on the whole. It can be concluded that the DE and ACO algorithms are considerably more adaptive in optimizing the forecasting problem for the HNN model. [ABSTRACT FROM AUTHOR]

This paper introduces a novel memetic algorithm namely Fractional Particle Swarm Optimization-based Memetic Algorithm (FPSOMA) to solve optimization problem using fractional calculus concept. The FC illustrates a potential for interpreting progression of the algorithm by controlling its convergence. The FPSOMA accomplishes global search over the whole search space through PSO whereas local search is performed by PSO with fractional order velocity to alter the memory of best location of the particles. To assess the performance of the proposed algorithm, firstly an empirical comparison study is presented for solving different test functions adopted from literature. Comparisons demonstrate the preference of FPSOMA than other related algorithms. Subsequently, experiments are conducted to achieve optimal gains of Fractional Order Proportional-Integral-Derivative (FO PID) controller in solving tracking problem. Results verify the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is a recently developed and widely used metaheuristic to perform global optimization tasks. Due to its simplicity in structure and reasonable performance, it has been utilized to solve several real-world applications. This paper proposes an alternate version of the SCA by adopting the greedy approach of search, crossover and exponentially decreased transition control parameter to overcome the issues of low exploitation, insufficient diversity and premature convergence. The proposed algorithm, called ECr-SCA, is validated and compared with the original SCA using computational time, diversity, performance index, statistical and convergence analysis on a set of 23 standard benchmark problems. Later, the proposed ECr-SCA is compared with seventeen other algorithms including improved versions of the SCA and state-of-the-art algorithms. Furthermore, the ECr-SCA is used to train multi-layer perceptron and the results are compared with variants of SCA and other metaheuristics. Overall comparison based on several different metrics illustrates the significant improvement in the search strategy of the SCA by the proposal of the ECr-SCA. • The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization. • A greedy search and modified transition parameter are used to increase exploitation. • The multi-parent crossover is embedded to enhance diversity and global search. • The excellent performance of the ECr-SCA is validated on benchmark problems. • The ECr-SCA is also used to train multilayer perceptron. [ABSTRACT FROM AUTHOR]

• Hybridized Crow search algorithm (CSA) and Artificial Bee Colony (ABC). • Used three mechanisms to improve CSA and ABC known as FRCSA-ABC. • Tested on benchmark and Combined Heat and Economic Dispatch problems. • Validated its search capability and robustness in cost-effectiveness. • Conducted non-parametrical tests to prove its efficient optimization. This paper attempts to obtain optimal generation scheduling for Combined Heat and Power Economic Dispatch (CHPED) problems and seeking a possible solution for the global optimization of test systems. As such, a Fuzzy adaptive Ranking- based Crow Search Algorithm (FRCSA) is amalgamated with modified Artificial Bee Colony (ABC). The proposed algorithm (FRCSA-ABC) has been integrated with three mechanisms in order to achieve competitive results effectively. The first mechanism uses the Fuzzy logic Inference System (FIS) to tune dynamically the parameters of flight length (fl) and awareness probability (CAP) of each crow. The second mechanism emerges from a natural phenomenon known as proximate optimality principle (POP) which reveals that better individuals will often have beneficial information and more probabilities to select and guide other individuals. This observation leads to the feasibility-based Ranking Crow Search Algorithm (RCSA) which generates a new food source from the chosen higher rankings of parent food sources. The third mechanism integrates the two modified global search phases of ABC with a local search phase of FRCSA to ensure promising performance. During the evaluation process, this mechanism investigates the aging level of the individual's best solution (p best) in order to choose an appropriate search phase in FRCSA and two modified phases of ABC. The performance of the algorithm is tested on four CHPED test systems, 23 well-known benchmark functions, and test suit of CEC2017. The results obtained are compared with its variants as well as with existing different approaches to validate its optimal search efficiency. Further, non-parametric statistical tests such as pair-wise and multiple comparisons tests are adapted to establish the supremacy of the FRCSA-ABC. The seminal aspect of this intended algorithm is that it can achieve cost-effectiveness conforming to meticulous global convergence. [ABSTRACT FROM AUTHOR]

This paper presents a novel motion planning algorithm for modular robots moving in environments with diverse terrain conditions. This requires the planner to generate a suitable control signal for all actuators, which can be computationally intensive. To decrease the complexity of the planning task, the concept of motion primitives is used. The motion primitives generate simple motions like ‘crawl-forward’ or ‘turn-left’ and the motion planner constructs a plan using these primitives. To preserve the efficiency and robustness of the planner on varying terrains, a novel schema called RRT-AMP (Rapidly Exploring Random Trees with Adaptive Motion Primitives) for adapting the motion primitives is introduced. The adaptation procedure is integrated into the planning process, which allows the planner simultaneously to adapt the primitives and to use them to obtain the final plan. Besides adaptation in changing environments, RRT-AMP can adapt motion primitives if some module fails. The methods is experimentally verified with robots of different morphologies to show its adaptation and planning abilities in complex environments. [ABSTRACT FROM AUTHOR]

Generation of electricity from solar energy has gained worldwide acceptance due to its abundant availability and eco-friendly nature. Even though the power generated from solar looks to be attractive; its availability is subjected to variation owing to many factors such as change in irradiation, temperature, shadow etc. Hence, extraction of maximum power from solar PV using Maximum Power Point Tracking (MPPT) method was the subject of study in the recent past. Among many methods proposed, Hill Climbing and Incremental Conductance MPPT methods were popular in reaching Maximum Power under constant irradiation. However, these methods show large steady state oscillations around MPP and poor dynamic performance when subjected to change in environmental conditions. On the other hand, bio-inspired algorithms showed excellent characteristics when dealing with non-linear, non-differentiable and stochastic optimization problems without involving excessive mathematical computations. Hence, in this paper an attempt is made by applying modifications to Particle Swarm Optimization technique, with emphasis on initial value selection, for Maximum Power Point Tracking. The key features of this method include ability to track the global peak power accurately under change in environmental condition with almost zero steady state oscillations, faster dynamic response and easy implementation. Systematic evaluation has been carried out for different partial shading conditions and finally the results obtained are compared with existing methods. In addition, simulations results are validated via built-in hardware prototype. [ABSTRACT FROM AUTHOR]

Parameter reduction in soft set is a combinatorial problem. In the past, the problem of normal parameter reduction in soft set is usually be solved by deleting dispensable parameters, that is, by the trial and error method to search the dispensable parameters. This manual method usually need much time to reduce unnecessary parameters, and the method is more suitable for small data. For the large data, however, it is impossible for people to reduce parameters in soft set. In this paper, the particle swarm optimization is applied to reduce parameters in soft set. Firstly, a definition is introduced to define the dispensable core, and some cases about the dispensable core are discussed. Then the normal parameter reduction model is built and the particle swarm optimization algorithm is employed to reduce the parameters. Experiments have shown that the method is feasible and fast. [ABSTRACT FROM AUTHOR]

Dynamic task allocation in a robotic swarm is a necessary process for proper management of the swarm. It allows the distribution of the identified tasks to be performed, among the swarm of robots, in such a way that a pre-defined proportion of execution of those tasks is achieved. In this context, there is no central unit to take care of the task allocation. So any algorithm proposal must be distributed, allowing every, and each robot in the swarm to identify the task it must perform. This paper proposes a distributed control algorithm to implement dynamic task allocation in a swarm robotics environment. The algorithm is inspired by the particle swarm optimization. In this context, each robot that integrates the swarm must run the algorithm periodically in order to control the underlying actions and decisions. The algorithm was implemented on ELISA III real swarm robots and extensively tested. The algorithm is effective and the corresponding performance is promising. [ABSTRACT FROM AUTHOR]

The run time for many optimisation algorithms, particularly those that explicitly consider mul- tiple objectives, can be impractically large when applied to real world problems. This paper reports an investigation into the behaviour of Multi-Objective Particle Swarm Optimisation (MOPSO), which seeks to reduce the number of objective function evaluations needed, with- out degrading solution quality. By restricting archive size and strategically reducing the trial solution population size, it has been found the number of function evaluations can be reduced by 66.7% without significant reduction in solution quality. In fact, careful manipulation of algorithm operating parameters can even significantly improve solution quality. [ABSTRACT FROM AUTHOR]

Recently more research works are focused on multi-objective particle swarm optimization algorithm (MOPSO) due to its ability of global and local search for solving multi-objective optimization problems (MOOPs); however, most of existing MOPSOs cannot achieve satisfactory results in solution quality. This paper proposes an efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search (MOLS), named MOLS-MOPSO, to deal with such problem. MOLS-MOPSO combines an effective particle updating strategy with the local search of MOLS. The effective particle updating strategy is used for global search to deal with premature convergence and diversity maintenance within the swarm; the MOLS is periodically activated for fast local search to converge toward the Pareto front. The exploratory capabilities are enhanced more efficiently by keeping a desirable balance between global search and local search, so as to ensure sufficient diversity and well distribution amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. Comparing MOLS-MOPSO with various state-of-the-art multi-objective optimization algorithms developed recently, the comparative study shows the effectiveness of MOLS-MOPSO, which not only assures a better convergence to the Pareto frontier but also illustrates a good diversity and distribution of solutions. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. The most important features of the PSO are easy implementation and few adjustable parameters. A novel PSO method called LHNPSO, with low-discrepancy sequence initialized particles and high-order (1/ π 2 ) nonlinear time-varying inertia weight and constant acceleration coefficients, is proposed in this paper. The initial population particles are generated by using the Halton sequence to fill the search space efficiently. Nonlinear functions with orders varied within big ranges are employed to adjust the inertial weight, cognitive and social parameters. Based on the sensitivity analysis of PSO performance to the changes of the orders of these nonlinear functions, 1/ π 2 order nonlinear function is selected to adjust the time-varying inertia weight and the two acceleration coefficients are set to be constants. A set of well-known benchmark optimization problems is then used to investigate the performance of the proposed LHNPSO algorithm and facilitate the comparison with other three types of PSO algorithms. The results show that the easily implemented LHNPSO can converge faster and give a much more accurate final solution for a variety of benchmark test functions. [ABSTRACT FROM AUTHOR]

The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction.The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement.This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of digital IIR filter design. Two novel modifications are proposed to Particle Swarm Optimization and validated through novel application for design of IIR filter. First modification is based on quantum mechanics and proved to yield a better performance. The second modification is to take care of time dependency character of the constriction factor. Extensive simulation results validate the superior performance of proposed algorithms. [ABSTRACT FROM AUTHOR]

To overcome computerized intractability and imprecise estimation of the standard cardinalized probability hypothesis density (CPHD) filter for multitarget tracking (MTT), an improved CPHD filtering algorithm is proposed in this paper. We apply Sequential Monte Carlo (SMC) method to achieve the closed-form solution in the filtering process as well as to avoid missed detection. Afterwards we partition the particle set into surviving particles and newborn particles based on the particle labels. To eliminate the over-estimated target number, the weights of newborn particles are assigned to increase to surviving particles on average. Simulations are presented to compare the performance of the proposed filtering algorithm with that of the standard one. The results show that the proposed filtering algorithm can effectively achieve MTT with better performance. [ABSTRACT FROM AUTHOR]

To ensure security during the excavation process of an earth pressure balance shield, this paper presents an optimal control method that accounts for the tunnel face's stability. The tunnel face is controlled by an optimal screw conveyor speed derived from the particle swarm optimization algorithm for a designed stable normal vector angle range on the distribution surface of the chamber pressure field. These normal vector angles can be computed online by measuring the changes to the earth pressure in the shield's chamber using a BP neural network model of the chamber pressure field distribution. An experimental example that uses excavation data from an actual EPB shield is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Combined Heat and Power Economic Dispatch (CHPED) is a real-world optimization problem with several complex constraints that has been a topic of studies around energy systems and optimization processes. This paper attempts to conceptualize a potent algorithm by combining the Modified Grasshopper Optimization Algorithm (MGOA) and the Improved Harris Hawks Optimizer (IHHO) for attaining a better balance between the beginning stages of global search and the latter stages of global convergence. The proposed attempt is abbreviated as MGOA-IHHO. Firstly, the chaotic and Opposition-Based Learning (OBL) methods are invoked to generate the initial population. Second, the mathematical model of the conventional Grasshopper Optimization Algorithm (GOA) is modified using Sine–Cosine Acceleration Coefficients (SCAC) to simulate the global exploration at the initial iterations and graduating to the global convergence at the final stages of optimization. Hence, it is named MGOA. Finally, the adaptive search mechanism integrates the two improved search phases of HHO with a search phase of MGOA to improve the performance of the proposed optimization method. This mechanism investigates the best solution for the aging level of the individual during the optimal evaluation process for choosing an appropriate search phase in MGOA-IHHO. The intended effect of the proposed MGOA-IHHO method is verified with other nature-inspired methods on standard single-objective test functions including 23 benchmark problems, 30 test suits of IEEE Congress on Evolutionary Computation 2017 (CEC2017), and four CHPED problems. The statistical results ascertain that the proposed hybridized MGOA-IHHO is capable of providing promising results when compared with its variants and optimization algorithms introduced in the literature. [Display omitted] • A hybrid Grasshopper optimization algorithm and Harris Hawks optimizer is proposed. • Three mechanisms are applied in the proposed hybrid MGOA-IHHO algorithm. • The MGOA-IHHO algorithm is tested on benchmark functions and CHPED problems. • The results are evaluated based on search capability in cost-effectiveness analysis. • Non-parametrical tests are conducted to demonstrate the efficiency of the MGOA-IHHO. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithm has attracted considerable attention because of its effectiveness and simplicity. However, previous studies have validated that DE still suffers from some limitations such as premature convergence and slow convergence especially dealing with multimodal optimization problems. To address these concerning issues, we propose an innovative optimization method named particle swarm-differential evolution algorithm with multiple random mutation (MRPSODE) in this paper. The proposed MRPSODE algorithm is based on multiple random mutation framework cooperating with mean particle swarm mutation strategy, DE/current-to-rand/1 mutation strategy and disturbance strategy. Firstly, we incorporate the modified mean particle swarm mutation strategy into DE algorithm to improve the global convergence ability. Secondly, DE/current-to-rand/1 mutation strategy is adopted to increase the population diversity and produce perturbations to avoid the algorithm trapping into a local optimum. Thirdly, we propose a disturbance strategy to help the population escape from local optima, so as to enhance the exploration ability. Finally, to ensure that the proposed algorithm can get satisfactory solutions with a fast convergence speed, we design a multiple random mutation framework, in which these three mutation strategies can effectively play their advantages and make up for the shortcomings of others. To evaluate the performance of the proposed algorithm, three different experiments are constructed on twenty-nine classical benchmark functions. The simulation results demonstrate that, (1) MRPSODE significantly outperforms conventional PSO and DE algorithms, (2) MRPSODE can achieve better performance than nine well-known DE variants in terms of solution quality and robustness, (3) MRPSODE is superior to nine latest heuristic-based algorithms. Furthermore, MRPSODE is successfully applied to seven typical constrained optimization problems and performs better than almost all compared methods. • A hybrid particle swarm differential evolution algorithm with multiple random mutation (MRPSODE) is proposed. • Three different mutation strategies are incorporated into DE algorithm to improve the optimization performance. • A random mutation framework is proposed to combine these three different mutation strategies. • Three kinds of experiments are conducted to validate the effectiveness of the MRPSODE. • The MRPSODE is successfully applied to seven constrained optimization problems. [ABSTRACT FROM AUTHOR]

In order to identify the source term of gas emission in atmosphere, an improved hybrid algorithm combined with the minimum relative entropy (MRE) and particle swarm optimization (PSO) method was presented. Not only are the estimated source parameters obtained, but also the confidence intervals at some probability levels. If only the source strength was required to be determined, the problem can be viewed as a linear inverse problem directly, which can be solved by original MRE method successfully. When both source strength and location are unknown, the common gas dispersion model should be transformed to be a linear system. Although the transformed linear model has some differences from that in original MRE method, satisfied estimation results were still obtained by adding iteratively adaptive adjustment parameters in the MRE-PSO method. The dependence of the MRE-PSO method on prior information such as lower and upper bound, prior expected values and noises were also discussed. The results showed that the confidence intervals and estimated parameters are influenced little by the prior bounds and expected values, but the errors affect the estimation results greatly. The simulation and experiment verification results showed that the MRE-PSO method is able to identify the source parameters with satisfied results. Finally, the error model was probed and then it was added in the MRE-PSO method. The addition of error model improves the performance of the identification method. Therefore, the MRE-PSO method with adjustment parameters proposed in this paper is a potential good method to resolve inverse problem in atmosphere environment. [ABSTRACT FROM AUTHOR]

Abstract: Interactive Multi-Objective Optimisation is an increasing field of evolutionary and swarm intelligence-based algorithms. By involving a human decision, a set of relevant non-dominated points can often be acquired at significantly lower computational costs than with a posteriori algorithms. A rarely addressed issue in interactive optimisation is the design of efficient user interfaces and the application of interactive optimisation as a design tool in engineering applications. This paper presents advances in the design of user interaction and and user interfaces in combination with interactive multi-objective particle swarm optimisation algorithm. The case study used for this research is an actively investigated aeronautics engineering problem. [Copyright &y& Elsevier]