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Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, artificial immune algorithm has become a research hotspot in the field of multi-objective optimization after the evolutionary algorithms. In this paper, most MOIAs can be classified into three main categories according to the type of problem solving, i.e., they are mostly designed to solve multi-objective optimization problems (MOPs), dynamic MOPs, and constrained MOPs. In this paper, a comprehensive survey is presented to summarize most existing MOIAs, in which their corresponding characteristics, principles and theoretical analyses are discussed in details. Moreover, the performance of MOIAs on solving various kinds of MOPs and many-objective optimization problems is also studied in our experimental comparisons. Finally, a brief conclusion is given to summarize the current drawbacks, challenges, and some future directions for MOIAs. [ABSTRACT FROM AUTHOR]

This paper presents a number of power grid network design and optimization techniques that consider the electromigration (EM) effects for multi-segment interconnect wires. First, we consider a new EM immortality constraint due to EM void saturation volume for multi-segment interconnects. It helps reduce conservativeness in the EM-aware on-chip power grid design. Along with the EM nucleation phase immortality constraint, we show that both EM immortality constraints can be naturally integrated into the existing programming based power grid optimization framework. Second, to mitigate the overly conservativeness of the immortality constrained optimization methods, we further explore three strategies: we first size up failed wires to meet one of the immortality conditions subject to the design rules; second, we consider the EM-induced aging effects on power supply networks for a target lifetime, which allows some short-lived wires to fail and optimizes the remaining wires; third, we propose a large change sensitivity-based optimization scheme to perform localized fixing based on recently proposed coupled EM-IR drop analysis method. Numerical results on a number of IBM-format power grid networks demonstrate that the new method can reduce more power grid area compared to the existing EM immortality constrained optimizations. Moreover, the new method is able to optimize power grids with nucleated wires, which would not be possible with the existing methods. Results also show the sensitivity-based localized power girds fixing can fix EM-induced IR drop violations in a few minutes for synthesized power grid networks from ARM core designs. • This paper introduces new power grid design and optimization techniques considering EM aging effects for a target lifetime. • A new EM immortality constraint due to EM void saturation volume is integrated into the framework • Three strategies are explored to further mitigate the conservativeness of the immortality constrained optimization method. • Experimental results validated the soundness of the proposed methods. [ABSTRACT FROM AUTHOR]

A periodic close encounter orbit is an orbit that periodically surveys a target spacecraft. The design of such orbits is important for both military and civil missions. As an example, it can be used for a preliminary mission design to better plan the repair of damaged, expensive, strategic, or extremely critical space assets. Indeed, this paper proposes an optimization approach and algorithm to reach periodic close encounter orbits through n -impulse maneuvers within the framework of Keplerian motion. The optimization is constrained to several bound requirements, such as a) distance bounds at encounter, b) total Δ v tot cost, c) single impulse cost Δ v k , d) encounter illumination angle, e) daily observation time, f) minimum perigee radius, g) maximum repetition time, h) maximum eccentricity, and i) maximum encounter time. The optimization, performed using Genetic Algorithm, has been validated by several numerical examples using the TLEs of existing satellites. The simulations showed good results and the reliability of the proposed approach via the Genetic Algorithm has been validated by a comparison with the Particle Swarm Optimization algorithm. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problem (CMOP) considers the convergence, diversity and feasibility of the population in the optimization process, so it is challenging to find desirable solutions of CMOP. Existing evolutionary multi-objective optimization algorithms have good performance on unconstrained multi-objective optimization problems, but have difficulties in solving CMOPs in discrete feasible regions. Aiming at this issue, this paper proposes a novel multi-level population hybrid search evolution algorithm (MLHSEA). First, a new multi-level hybrid search strategy (i.e. MHSS) is designed in the algorithm, which divides the population into three-level subpopulations based on Pareto ranks, constraint violation degree values, and feasible thresholds. Each subpopulation has its own unique evolution strategy to maximize the evolutionary potential of each subpopulation, which is beneficial to make some feasible solutions break through the discrete feasible region and reach the Pareto frontier. Then, a new population fusion degree strategy (i.e. PFDS) is proposed to timely perform population fusion and information exchange according to the population fusion degree (PFD) of each sub-population, thus improving the searchability of the target space. Finally, a novel detection reset strategy (i.e. DRS) is proposed for the lowest inferior subpopulation. This strategy can make inferior subpopulations avoid unnecessary evolutionary iterations and improve population diversity. Based on constrained test suites with four different characteristics, the experimental results show that the proposed MLHSEA outperforms other state-of-the-art constrained multi-objective optimization algorithms in performance. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the problem of expertise matching with various constraints. Expertise matching, which aims to find the alignment between experts and queries, is a common problem in many applications such as conference paper–reviewer assignment, product–reviewer alignment, and product-endorser matching. Most existing methods formalize this problem as an information-retrieval problem and focus on finding a set of experts for each query independently. However, in real-world systems, various constraints are often needed to be considered. For example, in order to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?” This paper explores such an approach by formulating the expertise matching problem in a constraint-based optimization framework. In the proposed framework, the problem of expertise matching is linked to a convex cost flow problem, which guarantees an optimal solution under various constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems, namely conference paper–reviewer assignment and teacher–course assignment. Experimental results validate the effectiveness of the proposed approach. Based on the proposed method, we have also developed an online system for paper–reviewer suggestions, which has been used for paper–reviewer assignment in a top conference and feedbacks from the conference organizers are very positive. [Copyright &y& Elsevier]

• A new advanced whale optimization algorithm for global optimization is proposed. • Armed force program and strategic adjustment are integrated with the original method. • CEC 2014 functions are tested to verify the effectiveness of the proposed optimizer. • The proposed optimizer is compared with others in solving engineering problems. In this paper, we propose a new whale army optimization algorithm with a view to solving multifarious optimization problems. The key novelty of our approach is to modify the original whale optimization algorithm to make it effective to solve the complicated, large-scale and constrained optimization problems. Our modifications mainly embody two aspects: the beneficial strategic adjustment to set key parameters and to help establish base principles in the original optimizer and the introduction of armed force program which classifies the search whales into different categories to achieve efficient cooperation. We evaluate the performance of the proposed algorithm, using three simple benchmark test functions over thirty CEC-2014 real-parameter numerical optimization problems and three constraint engineering design problems. The test results indicate that this algorithm can provide a faster local convergence rate, a higher convergence accuracy, and a lower computational complexity in comparison to traditional whale optimization algorithms and other sophisticated state of the art whale optimizers. Performance wise, it also surpasses many advanced methods for large-scaled complex functions. Furthermore, in this paper we propose a variant of whale army optimization algorithm to specifically address and solve optimizing constrained problems with a high degree of precision. [ABSTRACT FROM AUTHOR]

Modeling interdependencies in a production process is a vital aspect of process engineering. Being built on the fundamental understanding of the process and capturing underlying physical and chemical mechanisms, this task is traditionally carried out through first-principle modeling. However, due to various assumptions and parameter approximation, such approach sometimes struggles to accurately model process dynamics. This is partially due to the requirements for process stability in mission-critical scenarios, where it is not viable to conduct extensive experiments that would enable the development of comprehensive models, and hence, the process control is still partially dependent on the experience of the process operator. In this paper, a data-driven approach based on production data is introduced to address the challenge of discovering, understanding, and modeling interdependencies in a production process by constructing a constrained multi-objective optimization problem. The applicability of the proposed approach is demonstrated through a case study in the ironmaking industry, whereby a set of Machine Learning and causality-based methods is provided while highlighting the significance of transparency and use of domain knowledge in the development of data-driven models. Based on the real data from a Sintering plant, interdependencies are quantified between five key process parameters, which, when supplemented with other control parameters, result in a higher overall process output leading to reductions in operational costs and environmental impact. Such approach shows potential for applications in a broader context across other fields of science and engineering, where it can be used to improve the discovery of interdependencies in complex real-world industrial settings. • Modeling interdependencies in production is a vital aspect of process understanding. • First-principle models can struggle to accurately model complex process dynamics. • Constrained multi-objective optimization is used to address interdependency modeling. • Interdependencies are identified and quantified between five key parameters. • Higher output, reduced operational costs, and environmental impact are achieved. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ɛ -constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm. • Design a data-driven optimization method for the constrained controller tuning. • Design a gradient-based depth-first search strategy for strict constraints. • BA integrates a novel search mechanism to prevent premature convergence. • An improved boundary constraint handling scheme is proposed. [ABSTRACT FROM AUTHOR]

Highlights • A new Nash optimization method based on alternate elitist information exchange is presented. • Nash solutions between symmetric and alternate elitist information exchange is equivalent. • The Nash solution is nothing to do with the way of information exchange. • The Nash solution is only concerned with the information that is exchanged. • The methodology developed can be easily extended to MDO on HPC environments. Abstract Traditionally, it is considered that Nash equilibrium is based on the symmetric elitist information exchange. This paper presents a new Nash optimization method based on alternate elitist information exchange, it makes the information exchange and decision-making more convenient. Moreover, the subsequent decision-making can get the reward from the decision of the previous one, so the new method is more flexible in numerical implementation than that of the traditional symmetric elitist information exchanged algorithm. The equivalence of the solution of the new algorithm with the Nash optimization problem is proved in the paper. Then it is combined with evolutionary algorithms and/or adjoint methods to solve not only unconstrained but also constrained multi-objective optimization problems in aerodynamics. Numerical results show the potential efficiency and flexibility of new algorithms in solving complex multi-objective/disciplinary optimization problems in aeronautics. [ABSTRACT FROM AUTHOR]

Highlights • We design a shared control framework for driver steering override applied to automated driving. • The framework achieves a seamless and smooth control transfer between the driver and the system through a model predictive controller. • The framework features driver lane-change detection such that the system can assist the driver in lane-change maneuver. • Experimental results demonstrated the performance of the design and to suggest future directions for the design of driver override concepts. Abstract Recent research efforts on automated driving reveal the need to address driver's interaction with vehicle automation. This paper focuses on the design of an override mode for automated driving systems, a topic little studied in literature. In this paper, a shared control framework for driver's override of automatic steering control is proposed. This framework formulates the control transfer between the driver and the system as a constrained optimization problem which is solved online by a model predictive controller. At a higher level in the framework, the system detects driver's lane-change intention so that the low-level controller can actively assist the driver in a lane-change maneuver. The whole framework was evaluated in a user test based on a driving simulator. The test results show that the proposed shared control framework has the advantage of allowing the driver to regain control with ease while ensuring the smoothness of control transition. [ABSTRACT FROM AUTHOR]

Abstract The development of the meta-heuristic algorithms for solving the optimization problems and constrained engineering problems is one of the topics of interest to researchers in recent years. Particle swarm optimization algorithm (PSO) is one of the social search-based and swarm intelligence algorithms that is distinguished by its high speed, low number of parameters and easy implementation. However, the PSO algorithm has disadvantages such as finding the local minimum instead of the global minimum and debility in global search capability. In this article, in order to solve these deficiencies, the PSO algorithm is combined with position updating equations in Sine Cosine Algorithm (SCA) and the Levy flight approach. Therefore, a new hybrid method called PSOSCALF is introduced in this paper. In the SCA algorithm, the mathematical formulation for the solution updating is based on the behavior of sine and cosine functions. These functions guarantee the exploitation and exploration capabilities. Levy flight is a random walk that produces search steps using Levy distribution and then, with large jumps, more effective searches are occurred in the search space. Thus, using combination of the SCA and Levy flight in the PSOSCALF algorithm, the exploration capability of the original PSO algorithm is enhanced and also, being trapped in the local minimum is prevented. The performance and accuracy of the PSOSCALF method have been examined by 23 benchmark functions of the unimodal and multimodal type and 8 constrained real problems in engineering. The optimization results of the test functions show that the PSOSCALF method is more successful than the PSO family and other algorithms in determining global minimum of these functions. Also, the proposed PSOSCALF algorithm is successfully applied to the real constrained engineering problems and provides better solutions than other methods. Highlights • This paper proposes a new hybrid optimization algorithm called PSOSCALF. • Performance is tested using benchmark functions and constrained engineering problems. • Proposed algorithm is compared with well-known optimization algorithm. • The results show that the PSOSCALF is more effective than other methods. • Superiority of PSOSCALF is also proved by applying on engineering design problems. [ABSTRACT FROM AUTHOR]

This paper presents an approach using Pigeon Inspired Optimization (PIO) for selective harmonic elimination in a cascaded H-bridge (CHB) multilevel inverter fed with unequal dc sources. The aim of this work is to find the optimal combination of switching angles, such that the lower order harmonics are eliminated and the output voltage is constant irrespective of voltage change in the input side. This paper the PIO has been used to find the optimal angles for a 7-level inverter and the method can be scaled to any number of levels. To show the effectiveness of PIO the results have been compared with the results obtained using genetic algorithm (GA). The results show that the PIO is the most accurate and fastest evolutionary algorithm for switching angle optimization for the case of un-equal dc voltages. [ABSTRACT FROM AUTHOR]

• An optimum-pursuing method is developed for constrained optimization and reliability-based design optimization problems. • An optimum-pursuing function is proposed from the viewpoint of the optimization theory. • The well-known U function is rigorously derived based on the proposed optimum-pursuing function. • Optimum-pursuing method outperforms alternative methods in terms of both accuracy and efficiency. This paper proposes a new active learning method named as optimum-pursuing method (OPM) from the viewpoint of optimization theory, which aims to provide an effective tool for solving constrained optimization and reliability-based design optimization (RBDO) problems with low computation cost. It uses the cheap Kriging metamodel to replace the expensive physical response. The novelty of the proposed OPM primarily lies in two aspects. First, the OPM utilizes the advantage of the optimization theory rather than sampling technology. By using the augmented Lagrangian approach, it comprehensively considers the objective, constraints, and their relations, thereby automatic identification of important region in the vicinity of the optimum. Second, the accordingly optimum-pursuing function consists of three parts: Kriging mean, Kriging standard deviation, and merit function. Also, the target reliability surface is further considered to enhance the local accuracy of the reliability analysis. The performance of OPM is tested for both deterministic optimization and problems, in which two mathematical and three real-world engineering examples are selected to showcase the feasibility and validity. The results demonstrate that OPM is promising for solving both deterministic optimization and RBDO problems by comparing with the well-known active learning methods. [ABSTRACT FROM AUTHOR]

Evolutionary computing is an exciting sub-field of soft computing. Many evolutionary algorithm based on the Darwinian principles of natural selection are developed under the umbrella of EC in the last two decades. EAs provide a set of optimal solutions in single simulation unlike traditional optimization techniques for dealing with large-scale global optimization and search problems. Teaching Learning based Optimization (TLBO) is one of the most recently developed EA. TLBO employs a group of learners or a class of learners to perform global optimization search process. The framework of the TLBO consists of two phases, including the Teacher Phase and Learner Phase. The Teacher Phase' means learning from the teachers and the Learner Phase means learning through interaction among learners. In this paper, we have developed a hybrid TLBO (HTLBO) with aim at to further improve the exploration and exploitation abilities of the baseline TLBO algorithm. The performance of the proposed HTLBO algorithm examined upon using recently designed benchmark functions for the special session of the CEC2017 problems. The experimental results of the proposed algorithm are better than some well-known evolutionary algorithms in terms of proximity and diversity. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

• The advantages and limitations of the traditional emissivity assumption model method and the method without emissivity model are compared and analyzed. • Challenges of using radiation thermometry in practical engineering are illustrated. • Ideas and suggestions for the selection of multispectral radiation thermometry data processing methods in practical engineering are provided. Multispectral radiation thermometry is one of the most effective methods to obtain object true temperature and emissivity information in radiation thermometry. How to improve the measurement accuracy of multispectral radiation thermometry has always been driving the focus of the scientific community. This paper provides a comprehensive review of data processing algorithms in multispectral radiation thermometry, including the conventional emissivity assumption model method and the improved method which are exhibited from the aspects of temperature measurement accuracy, data processing time, and modeling form. This paper aims to describe and clarify the principles, accuracy, features, and application scenarios of the above data processing algorithms. Some algorithmic developments and recent advances are presented, which are based on the emissivity assumption model, neural networks, and constrained optimization theory. Relative advantages and limitations are outlined by comparing relevant studies of data processing algorithms in the literature. The review will help researchers to determine the algorithm suiting objects according to the temperature measurement range and application scenarios to improve the accuracy of multispectral radiation thermometry. Knowledge of the above data processing algorithms will also help researchers develop novel methods to promote the development of multispectral radiation thermometry. [ABSTRACT FROM AUTHOR]

This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search (GS) and opposition-based learning (OBL) to respectively increase the ChOA's capabilities for exploration and exploitation in addressing real practical engineering-constrained problems. In order to investigate the efficiency of the GSOBL-ChOA, its performance is evaluated by twenty-three standard benchmark functions, 10 benchmark functions from CEC06-2019, a randomly generated landscape, and 12 real practical Constrained Optimization Problems (COPs-2020) from a wide variety of engineering fields, including power system design, synthesis and process design, industrial chemical producer, power-electronic design, mechanical design, and animal feed ratio. The findings are compared to those obtained using benchmark optimizers such as CMA-ES and SHADE as state-of-the-art optimization techniques and CEC competition winners; standard ChOA; OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. In order to perform a comprehensive assessment, three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni–Dunn and Holm, and Friedman average rank tests, are utilized. The top two algorithms are GSOBL-ChOA and CMA-ES, with scores of forty and eleven, respectively, among 27 mathematical functions. jDE100 obtained the highest score of 100 in the 100-digit challenge, followed closely by DISHchain1e+12, which achieved the highest possible score of 97, and GSOBL-ChOA obtained the fourth-highest score of 93. Finally, GSOBL-ChOA and CMA-ES outperform other benchmarks in five and four real practical COPs, respectively. The source code of the paper can be downloaded using the following link: https://se.mathworks.com/matlabcentral/fileexchange/119108-evolving-chimp-optimization-algorithm-by-weighted-opposition. • Applying the opposition-based learning concept to chimp optimization algorithm. • Applying greedy search to accelerate the convergence rate. • The proposed method proposes a novel constraint handling technique. • The proposed method's performance was evaluated on 37 numerical functions. • The proposed method's performance was tested on 12 real-world constraint problems. [ABSTRACT FROM AUTHOR]

With the rapid development of financial market, a growing number of stocks become available on the financial market. How to efficiently select these stocks to achieve higher return and lower risk has become a hot research topic in financial management. This is usually called the portfolio optimization problem (POP). When the cardinality constrained (CC) is added to limit the number of selected stocks to a certain value, the resulting CCPOP is more challenging with the following two difficulties: i) Due to the complexity of CC in finical market, how to efficiently deal with CC in POP to obtain feasible solution is difficult and time-consuming. ii) The objectives of portfolio return and risk always conflict with each other and their relation is difficult to balance. To better deal with above difficulties, this paper focuses on the multi-objective CCPOP (MoCCPOP) and proposes a multiple populations co-evolutionary particle swarm optimization (MPCoPSO) algorithm, which is based on multiple populations for multiple objectives (MPMO) framework and has the following four advantages. Firstly, a hybrid binary and real (HBR) encoding strategy is introduced to better represent the stock selection and the asset weight of the solutions in MoCCPOP. Secondly, a return risk ratio heuristic (R3H) strategy based on the historical return and risk of each stock is proposed as a fast CC handling method to obtain feasible solutions. Thirdly, a new particle update method based on bi-directional local search (BLS) strategy is designed to increase the chance to improve the solution accuracy and to approach the global Pareto front (PF). Last but not least, a hybrid elite competition (HEC) strategy is proposed to assist the archive update, which provides more promising solutions and brings diversity to avoid local PF. The first two strategies help to efficiently deal with the CC challenge, while the last two strategies are efficient in solving the multi-objective challenge. By comparing with some recent well-performing and state-of-the-art multi-objective optimization algorithms, MPCoPSO shows the superior performance in solving the MoCCPOP. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of efficient constrained global optimization of hybrid models that are a composition of a known white-box function and an expensive multi-output black-box function subject to noisy observations, which often arises in real-world science, engineering, manufacturing, and control applications. We propose a novel method, Constrained Upper Quantile Bound (CUQB), to solve such problems that directly exploits the composite structure of the objective and constraint functions that we show leads substantially improved sampling efficiency. CUQB is a conceptually simple, deterministic approach that avoids the constraint approximations used by previous methods. Although the CUQB acquisition function is not available in closed form, we propose a novel differentiable sample average approximation that enables it to be efficiently maximized. We further derive bounds on the cumulative regret and constraint violation under a non-parametric Bayesian representation of the black-box function. Since these bounds depend sublinearly on the number of iterations under some regularity assumptions, we establish explicit bounds on the convergence rate to the optimal solution of the original constrained problem. In contrast to most existing methods, CUQB further incorporates a simple infeasibility detection scheme, which we prove triggers in a finite number of iterations when the original problem is infeasible (with high probability given the Bayesian model). Numerical experiments on several test problems, including environmental model calibration and real-time optimization of a reactor system, show that CUQB significantly outperforms traditional Bayesian optimization in both constrained and unconstrained cases. Furthermore, compared to other state-of-the-art methods that exploit composite structure, CUQB achieves competitive empirical performance while also providing substantially improved theoretical guarantees. [ABSTRACT FROM AUTHOR]

This paper presents a path generation method for automated vehicle based on Bezier curve. Derivation of smooth path is one of important subjects for vehicle navigation system. Bezier curve is a smooth parametric curve, but adequate assignment of its control points that determine the shape of Bezier curve is not a simple problem. In the proposed method, first n th-order base Bezier curve in N -dimension is derived with the end point constraints (location and velocity). As a next step, path modification is done by using constrained optimization with designated via-points. The presented method is tested by several calculation experiments in case of 2-dimensional and 3-dimensional path. The results are shown and discussed in this paper. [ABSTRACT FROM AUTHOR]

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html . [ABSTRACT FROM AUTHOR]

Real life optimization problems involve a number of constraints arising out of user requirements, physical laws, statutory requirements, resource limitations etc. Such constraints are routinely evaluated using computationally expensive analysis i.e., solvers relying on finite element methods, computational fluid dynamics, computational electro magnetic, etc. Existing optimization approaches adopt a full evaluation policy , i.e., all the constraints corresponding to a solution are evaluated throughout the course of search. Furthermore, a common sequence of constraint evaluation is used for all the solutions. In this paper, we introduce a novel scheme for constraint handling, wherein every solution is assigned a random sequence of constraints and the evaluation process is aborted whenever a constraint is violated. The solutions are sorted based on two measures i.e., the number of satisfied constraints and the violation measure. The number of satisfied constraints takes precedence over the amount of violation and the most efficient sequence of constraint evaluation is evolved during the course of search. The performance of the proposed scheme is rigorously compared with other state of the art constraint handling methods using single objective inequality constrained test problems of CEC-2006 and CEC-2010 . The constraint handling approach is generic and can be easily used for the solution of constrained multiobjective optimization problems or even problems with equality constraints. A bi-objective welded beam design problem, a tri-objective car side impact problem and an equality constrained optimization problem of CEC-2006 is solved as an illustration. The results clearly highlight potential savings offered by the proposed strategy and more importantly the paper provides a detailed insight on why such a strategy performs better than others. [ABSTRACT FROM AUTHOR]

• An innovative algorithm called CMFPA is proposed. • CMFPA is successfully tested well-known functions and CEC2017' functions. • CMFPA is used to solve engineering optimization problems in real life. • Experiments show that CMFPA is efficient and effective. The flower pollination algorithm (FPA) is a relatively new swarm optimization algorithm that inspired by the pollination phenomenon of natural phanerogam. Since its proposed, it has received widespread attention and been applied in various engineering fields. However, the FPA still has certain drawbacks, such as inadequate optimization precision and poor convergence. In this paper, an innovative flower pollination algorithm based on cloud mutation is proposed (CMFPA), which adds information of all dimensions in the global optimization stage and uses the designed cloud mutation method to redistribute the population center. To verify the performance of the CMFPA in solving continuous optimization problems, we test twenty-four well-known functions, composition functions of CEC2013 and all benchmark functions of CEC2017. The results demonstrate that the CMFPA has better performance compared with other state-of-the-art algorithms. In addition, the CMFPA is implemented for five constrained optimization problems in practical engineering, and the performance is compared with state-of-the-art algorithms to further prove the effectiveness and efficiency of the CMFPA. [ABSTRACT FROM AUTHOR]

This paper presents a deterministic optimization strategy to solve the economic-environmental power dispatch (EEPD) problem of an electrical power system, using a Broyden Fletcher Goldfarb Shanno based on the augmented Lagrangian (BFGS-AL) algorithm. This problem is a nonlinear constrained multi-objective optimization. The objectives of this optimization are minimized the total fuel cost and emission amount for thermal generators while satisfying electric power systems equality and inequality constraints. The proposed approach is applied on the standard IEEE 30 bus test system, where the power transmission losses are taken into account. Simulation results proved that the proposed approach gives better-quality solution in terms of accuracy and convergence to the best compromise solution compared to other deterministic and meta-heuristic optimization techniques. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm based on collective neurodynamic system is investigated for distributed constrained convex optimization, whose objective function is a sum of smooth convex functions and non-smooth L 1 -norm functions. Inspired by recent advances in distributed convex optimization, the continuous-time and discrete-time distributed optimization algorithms described by collective neurodynamic systems are proposed. In the systems, each of the smooth objective functions is allocated to each node as well as each of the L 1 -norm functions. However, the L 1 -norm functions are realized by projection operators. Meanwhile, each node satisfies the local linear and bound constraints. Then a connected network is constituted from all the nodes with consensus to find the optimal solutions. [ABSTRACT FROM AUTHOR]

Economic emission dispatch (EED) problem of an electrical power system can be considered as one of the most popular constrained multi-objective optimization problems to minimize the cost and emission simultaneously subjecting to various constraints. Although many approaches have been presented to deal with this problem, it is still a challenge issue especially when more and more renewable energy sources such as wind power and solar power are incorporated into the system due to their intermittence and uncertainty. To improve the EED performance with those renewable power generations, a constrained multi-objective population extremal optimization algorithm called CMOPEO-EED is proposed by utilizing an advanced constraint handling technique, i.e., the superiority of feasible solution approach. To demonstrate the effectiveness of the proposed method, three versions of a modified IEEE 30-bus and 6-generator system with renewable power generations are considered as the test systems. The comprehensive experimental results and analyses fully validate that the proposed CMOPEO-EED method in this paper outperforms these recently reported single-objective success history based adaptive differential evolutionary algorithm (SHADE)-based EED method and constrained non-dominated sorting genetic algorithm-based EED (CNSGAII-EED) method in terms of cost and emission indices. Image 1002310 • Three cases of renewable power are incorporated for EED problem. • Superiority of feasible solutions is embedded in CMOPEO to handle constraints. • CMOPEO is developed firstly for multi-objective EED problem. • Three test systems illustrate its superiority to CNSGAII-EED and SHADE. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a widely used heuristic algorithm for numerical optimization over continuous space. As the core operator, the mutation operator has a great effect on DE's performance. In this paper, we propose a cooperative ranking-based mutation strategy (CRM) for DE when solving constrained optimization problems (COPs). In CRM, two different ranking criteria are adopted in a cooperative way to move the population towards a feasible global optimum with a faster convergence speed. The first criterion is objective function value-based criterion which is applied for feasible solutions to converge towards the global optimum. The second criterion is overall constraint violation-based criterion which is applied for infeasible solutions to search for feasible regions. The proposed CRM is integrated into the basic DE and two advanced DE variants. The comparison results showed that the DE variants with CRM have a faster convergence speed than the non-CRM-based variants in the majority of test problems. This approach is a promising strategy to improve the search ability of DE variants when solving COPs. [ABSTRACT FROM AUTHOR]

To address the challenges that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these strategies require accurate network models to work effectively. However, distribution network data are known to contain errors, and attention has been given to techniques that allow to derive improved network information. This paper presents a novel method to derive line impedance values from smart meter measurement time series, with realistic assumptions in terms of meter accuracy, resolution and penetration. The method is based on unbalanced state estimation and is cast as a non-convex quadratically constrained optimization problem. Both line lengths and impedance matrix models can be estimated based on an exact nonlinear formulation of the steady-state three-phase network physics. The method is evaluated on both the IEEE European Low Voltage Feeder (906 buses) and a real Belgian LV feeder, showing promising results. • Impedance values and operating conditions (state estimation) are jointly derived. • Accurate unbalanced impedance models are derived from smart meter measurements. • Impedance estimation is cast as a quadratic constrained optimization problem. • Constraints are added to impose structure of multi-conductor impedance matrices. • Steady-state network physics is exploited: impedance estimation is model-aware. [ABSTRACT FROM AUTHOR]

• Discrete time Consensus Based Optimization (CBO) algorithm on a convex set is introduced. • The error between optimal value and consensus value is rigorously calculated and further extended from previous paper. • Portfolio optimization problem was solved using our proposed constrained CBO algorithm. In this paper, we propose a predictor-corrector type Consensus Based Optimization(CBO) algorithm on a convex feasible set. Our proposed algorithm generalizes the CBO algorithm in [11] to tackle a constrained optimization problem for the global minima of the non-convex function defined on a convex domain. As a practical application of the proposed algorithm, we study the portfolio optimization problem in finance. In this application, we introduce an objective function to choose the optimal weight on each asset in an asset-bundle, which yields the maximal expected returns given a certain level of risks. Simulation results show that our proposed predictor-corrector type model is successful in finding the optimal value. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new optimization technique called water cycle algorithm (WCA) which is applied to a number of constrained optimization and engineering design problems. The fundamental concepts and ideas which underlie the proposed method is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the real world. A comparative study has been carried out to show the effectiveness of the WCA over other well-known optimizers in terms of computational effort (measures as number of function evaluations) and function value (accuracy) in this paper. [Copyright &y& Elsevier]

This paper aims to study the application of a heuristic optimization technique namely, Invasive Weed Optimization (IWO) technique for optimal protection coordination in power systems. The optimal relay coordination problem is formulated as a nonlinear constrained optimization, which is solved using Improved IWO (IIWO). The proposed IIWO algorithm modifies the standard deviation expression of the weed population. The simulation results show that IIWO has faster and better convergence compared with standard IWO. To further improve the computational efficiency, a hybrid IIWO method is also proposed which is obtained by defining sequential quadratic programming (SQP) as a subroutine in IIWO for searching local solutions, thus eliminate weaker weeds in the colonization process. The proposed techniques are tested on both the 9-bus test system and IEEE- 30 bus systems and the performance is compared. Relay coordination algorithm is developed in MATLAB, and the results are found to be effective and reliable. • Introduction of the application of invasive weed optimization (IWO) algorithm to DOcR coordination problem. • Proposing a variant of IWO, called IIWO, which modifies the standard deviation expression to fit for DOcR coordination problem which provides faster and better convergence. • Proposing a Hybrid IIWO algorithm to find global solution to the subject problem. • Test the proposed algorithms on two test systems viz. the 9 bus, and IEEE 30 bus systems. • Provide a computational and statistical analysis of the obtained results to establish the superiority of the proposed heuristic and hybrid heuristic models. [ABSTRACT FROM AUTHOR]

Abstract This paper solves optimization problems where both the objective and constraints are given by fuzzy functions. In order to get it, we first prove that these problems are equivalent to optimization problems where the constraints functions are non-fuzzy functions and we introduce a new and wider stationary point concept that generalizes all existing concepts so far. This new stationary point concept is based on the gH -differentiability and has many computational advantages that we describe. It is well-known that obtain a useful differentiability notion for fuzzy functions is a difficult task without linearity. And we are in that case due to the fact that the fuzzy numbers (intervals) space is a nonlinear one. In this direction, the gH -derivative for fuzzy functions is a concept that is more general than Hukuhara and level-wise derivatives that are usually used in fuzzy optimization so far, in the sense that they can be applied to a wider number of fuzzy function classes than above concepts. With this new differentiability concept, we prove a necessary optimality condition for fuzzy optimization problems that is more operational and less restrictive that the few ones we can find in the literature so far. Moreover, due to the fact that we do not have a linear space for fuzzy numbers, the convex concepts and generalized convex fuzzy function notion are very restrictive, also. This implies that the sufficiency optimality conditions for fuzzy problems published so far are not useful. [ABSTRACT FROM AUTHOR]

• Inverse dynamic model is derived by using Jacobi-Anger identity. • The high-frequency components in the GyroWheel dynamics are dealt with. • The ill-conditioned problem of GyroWheel parameter identification is focused on. • A constrained optimization problem is established to identify the parameters. • Specific tests are designed to guide and constraint the optimization process. GyroWheel is a rate sensing/momentum management instrument for small spacecrafts. The challenge to realize accurate angular rate estimation consists in the identification of system parameters, which is investigated in this paper. An inverse dynamic model is derived by ignoring the high-frequency components, and it is the foundation of parameter identification. Then a two-step method is developed to solve the ill-conditioned problem of parameter identification. In the first step, the inverse dynamic identification model is simplified in some special situations, and a rough estimation of the parameters can be obtained. In the second step, a constrained optimization problem is obtained by introducing the results of the first step, and particle swarm optimization algorithm is utilized to realize the identification of these parameters. Validation simulations and experiments on a GyroWheel prototype show that the proposed method is a good promising method for offline parameter identification. [ABSTRACT FROM AUTHOR]

Abstract The integrity of engineering structures is often compromised by embedded surfaces that result from incomplete bonding during the manufacturing process, or initiation of damage from fatigue or impact processes. Examples include delaminations in composite materials, incomplete weld bonds when joining two components, and internal crack planes that may form when a structure is damaged. In many cases the areas of the structure in question may not be easily accessible, thus precluding the direct assessment of structural integrity. In this paper, we present a gradient-based, partial differential equation (PDE)-constrained optimization approach for solving the inverse problem of interface detection in the context of steady-state dynamics. An objective function is defined that represents the difference between the model predictions of structural response at a set of spatial locations, and the experimentally measured responses. One of the contributions of our work is a novel representation of the design variables using a density field that takes values in the range [ 0 , 1 ] and raised to an integer exponent that promotes solutions to be near the extrema of the range. The density field is combined with the penalty method for enforcing a zero gap condition and realizing partially bonded surfaces. The use of the penalty method with a density field representation leads to objective functions that are continuously differentiable with respect to the unknown parameters, enabling the use of efficient gradient-based optimization algorithms. Numerical examples of delaminated plates are presented to demonstrate the feasibility of the approach. Highlights • We present a method for identifying partially connected surfaces. • Our formulation leads to a differentiable objective function. • We use a density field representation of the design variables. • Our method can be applied to arbitrary geometries. [ABSTRACT FROM AUTHOR]

Highlights • The problem of an artificial rule for establishing segmentation fragments in empirical wavelet transform is presented. • Empirical wavelet transform for bearing fault diagnosis is formulated as a constrained optimization problem. • Segmentation fragments in empirical wavelet transform are optimized. • Fault signatures in squared envelope spectra obtained by empirical wavelet transform are jointly maximized. Abstract Empirical wavelet transform is a wavelet filter bank to decompose a bearing fault signal into several sub-signals for extracting bearing fault features and it attracts lots of attention recently because it looks like a hybrid of wavelet transform and empirical mode decomposition. However, an assumption for use of empirical wavelet transform is that segmentation fragments in empirical wavelet transform must be established in advance. To satisfy this assumption, an artificial rule based on local maxima of the frequency spectrum of a signal was previously proposed to establish segmentation fragments in empirical wavelet transform. Obviously, because the artificial rule completely relies on local maxima, it is not always reliable and not optimal to establish segmentation fragments in empirical wavelet transform for bearing fault feature extraction. In this paper, empirical wavelet transform for extracting bearing fault features is formulated as a constrained optimization problem. Segmentation fragments in empirical wavelet transform are optimized to jointly maximize bearing fault signatures in squared envelope spectra obtained by empirical wavelet transform. Industrial bearing fault signals are used to illustrate how the constrained optimization problem can find the optimal segmentation fragments in empirical wavelet transform. Comparisons with empirical wavelet transform guided by the artificial rule as well as two advanced fault diagnosis algorithms including spectral kurtosis and spectral correlation are made to experimentally demonstrate that the optimal segmentation fragments in empirical wavelet transform can result in higher detection performance. [ABSTRACT FROM AUTHOR]

Abstract: The present paper proposes an approach for identifying target reliabilities for components of complex engineered systems with given acceptance criteria for system performance. The target reliabilities for components must be consistent in the sense that the system performance resulting from the choice of the components’ reliabilities satisfy the given acceptance criteria, and should be optimal in the sense that the expected utility associated with the system is maximized. To this end, the present paper first describes how complex engineered systems may be modelled hierarchically by use of Bayesian probabilistic networks and influence diagrams. They serve as functions relating the reliabilities of the individual components of the system to the overall system performance. Thereafter, a constrained optimization problem is formulated for the optimization of the component reliabilities. In this optimization problem the acceptance criteria for the system performance define the constraints, and the expected utility from the system is considered as the objective function. Two examples are shown: (1) optimization of design of bridges in a transportation network subjected to an earthquake, and (2) optimization of target reliabilities of welded joints in a ship hull structure subjected to fatigue deterioration in the context of maintenance planning. [Copyright &y& Elsevier]

Abstract: This paper extends some duality results from a standard optimization setup to a noncooperative (Nash) game framework. A Nash game (NG) with coupled constraints is considered. Solving directly such a coupled NG requires coordination among possibly all players. An alternative approach is proposed based on its relation to a special constrained optimization problem for the NG-game cost function, with respect to the second argument that admits a fixed-point solution. Specific separability properties of the NG-game cost are exploited and duality results are developed. This duality extension leads naturally to a hierarchical decomposition into a lower-level NG with no coupled constraints, and a higher-level system optimization problem. In the second part of the paper these theoretical results are applied to a coupled NG with coupled constraints as encountered in optical networks. [Copyright &y& Elsevier]

Abstract: This paper presenting case studies in modern large scale constrained optimization, the purpose of which is to illustrate how recent advances in algorithms and modelling languages have made it easy to solve difficult optimization problems using of software. In this paper we use measure theory technique and iterative dynamic programming algorithm for solving of a trajectory optimization problem: how to drive a train so as to minimize fuel costs. In the first method, we optimal control problem to using of atomic measures change this one to an infinite dimensional linear programming problem and then we approximate the latter one to a finite dimensional linear programming problem, then by the optimal solution of the final problem we obtain sub-optimal controls and then by these controls we obtain the approximate solution of the original problem. In the second method, we will using dynamic programming as iteration for solving optimal control problem. [Copyright &y& Elsevier]

Constrained optimization (CO) has made a profound impact in solving many real-world problems. Due to the high computation burden in exact solvers, data-driven CO based on machine learning techniques is recently receiving extensive research interests for its capability to solve CO problems in real time. The existing data-driven CO approaches only serve for optimization problems with rather simple constraints that can be directly incorporated into model training. However, constraints that are computationally infeasible or burdensome to evaluate are commonly experienced in realistic optimization applications, especially in the engineering sector. This paper proposes an athlete–referee dual learning system (ARDLS) for end-to-end CO with large-scale complex constraints, where an athlete model is trained as the main optimizer while a referee model is trained as a probabilistic constraint classifier to guide the athlete training. A risk-based constrained loss function is designed to fine-tune the athlete model for constraint satisfaction. A case study on electric power system emergency control application is conducted to validate the proposed ARDLS, where the testing results demonstrate the excellent capability of ARDLS to improve the likelihood of satisfying large-scale complex constraints in CO. [ABSTRACT FROM AUTHOR]