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There are many efficient and effective constraint-handling mechanisms for constrained optimization problems. However, most of them evaluate all the individuals, including the worse individuals, which waste a lot of fitness evaluations. In this paper, halfspace partition mechanism based on constraint violation values is proposed. Since constraint violation information of individuals in current generation are already known, the positive side of tangent line of one point as positive halfspace is defined. A point is treated as potential point if it locates in the intersect region of two positive halfspaces. Hence, the region includes all these points has greater possibility to obtain smaller constraint violation. Only when the offspring locates in this area, the actual objective function value and constraint violation will be calculated. The estimated worse individuals will be omitted without calculating actual constraint violation and fitness function value. Four engineering optimization and a case study with the grinding optimization process are studied. The experimental results verify the effectiveness of the proposed mechanism. [ABSTRACT FROM AUTHOR]

This research aims at solving the special case of multistate assignment problem. The problem includes many special characteristics which are not normally included in the assignment problem. There are many types and conditions of vehicles included in the planning and different road conditions of traveling, which would have different effects on fuel consumption, which is the objective function of the study. The proposed problem is determined as a large and complicated problem making the optimization software unable to find an optimal solution within the proper time. Therefore, the researchers had developed a method for determining the optimal solution by using particle swarm optimization (PSO) in which the methods were developed for solving the proposed problem. This method is called the modified particle swarm optimization (modified PSO). The proposed method was tested with three groups of tested instances, i.e., small, medium, and large groups. The computational result shows that, in small-sized and medium-sized problems, the proposed method performed not significantly different from the optimization software, and in the large-sized problems, the modified PSO method gave 3.61% lower cost than the cost generated from best solution generated from optimization software within 72 hours and it gave 11.03% better solution than that of the best existing heuristics published so far (differential evolution algorithm). [ABSTRACT FROM AUTHOR]

The economic dispatch (ED) of distributed energy resources (DERs) is an important and challenging problem in smart grid operation. Different frameworks are proposed for solving this issue, among which the virtual power plant (VPP) is considered as a promising means for ED in smart grids with DERs. This study presents a novel optimisation algorithm entitled sequence-based differential evolution (SDE) for solving generation dispatch among several DERs with VPPs. In the proposed method, a novel and efficient initialisation technique, namely sequence-based deterministic initialisation, is used to generate high-quality initial population. Besides, a self-adaptation mechanism is utilised to eliminate the difficulty of tuning the problemspecific control parameters of the algorithm. Subsequently, the SDE is applied for solving the ED model to obtain the optimal power generation share of the VPP. Case studies compare the solutions obtained by the proposed method with several state-ofthe- art algorithms in the existing literature. It is validated the proposed method is advantageous for ED with VPPs in better solution accuracy, less computational burden, and faster convergence. [ABSTRACT FROM AUTHOR]

• A hybrid DE–MARS–based model is a good predictive model of the phosphorus and chlorophyll presences. • Algal abnormal proliferation is dangerous for environment in lakes. • The biological and physical-chemical input variables in this process are studied in depth. • The results of this hybrid model for the eutrophication evaluation is compared with the M5 model tree. • The obtained correlation coefficients of two hybrid DE–MARS–based models are about 90%. Algal atypical proliferation is a consequence of water fertilization (also called eutrophication) and one of the main causes of the degradation of reservoir and lake ecosystems. Its intensification during the last decades has led the stakeholders to seek water management and restoration solutions, including those based on modelling approaches. In this way, this paper presents one reservoir eutrophication modelling based on a new hybrid algorithm that combines multivariate adaptive regression splines (MARS) and differential evolution (DE) to estimate the algal abnormal proliferation from physical-chemical and biological variables. This technique involves the optimization of the MARS hyperparameters during the training process. Additionally, an M5 model tree was fitted to the experimental data for comparison purposes. Apart from successfully forecasting algal atypical growth (coefficients of determination equal to 0.83 and 0.91), the model showed here can establish the significance of each biological and physical-chemical parameter of the algal enhanced growth. Finally, the main conclusions of this research work are exposed. [ABSTRACT FROM AUTHOR]

This paper presents a coupled self-adaptive multiobjective differential evolution and network flow algorithm for the optimal operation of complex multipurpose reservoir systems. The developed algorithm (i.e., self-adaptive multiobjective differential evolution) is compared to nondominated sorting genetic algorithm II using a set of common test problems and a real-world case study. An out-of- kilter method for minimal-cost flow problems is used to optimize the water resource system from self-adaptive multiobjective differential evolution inputs driven by the evolutionary process. Self-adaptive multiobjective differential evolution is then used to evaluate objective functions based on the outputs from out-of-kilter algorithm and the process continues until the stop criterion is met. The advantages of the proposed approach include (1) flexible evolutionary algorithms for solving highly complex objective function, and (2) efficient network flow method for dealing with large and highly constrained problems. The case study includes one part of a complex water supply system located in southwestern Brazil that provides water for almost 20 million people in Sao Paulo metropolitan area. The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. The coupled model is applied to the case study using one inflow scenario representing a drought period with inflows below historical average. Multiobjective analyses are performed by comparing two pairs of objective functions, as follows: (1) minimization of demand shortage versus minimization of pumping cost, and (2) minimization of demand shortage versus minimization of the deviation from the water quality standards. The problem constraints include reservoir capacity, capacity of tunnels, channel flow limitations, and minimum downstream release for all reservoirs within the system. The proposed coupled model (self-adaptive multiobjective differential evolution and out-of-kilter) is outperforming both pure self-adaptive multiobjective differential evolution and nondominated sorting genetic algorithm II, as it requires significantly smaller number of generations to derive the Pareto front. In addition, the proposed approach is capable of handling larger problems without major computational burden. The coupled model and self-adaptive multiobjective differential evolution also converge closer to, and provide better coverage of the true Pareto front than, nondominated sorting genetic algorithm II. [ABSTRACT FROM AUTHOR]

Abstract: Magneto-electro-elastic structures are built from materials that provide them the ability to convert in an interchangeable way, magnetic, electric and mechanical forms of energy. This characteristic can therefore provide an adaptive behaviour to a general configuration elastic structure, being commonly used in association with any type of composite material in an embedded or surface mounted mode, or by considering the usage of multiphase materials that enable achieving different magneto-electro-elastic properties. In a first stage of this work, a few cases studies will be considered to enable the validation of the model considered and the influence of the coupling characteristics of this type of adaptive structures. After that we consider the application of a recent computational intelligence technique, the differential evolution, in a deflection profile minimization problem. Studies on the influence of optimization parameters associated to the problem considered will be performed as well as the adoption of an adaptive scheme for the perturbation factor. Results are also compared with those obtained using an enhanced particle swarm optimization technique. [Copyright &y& Elsevier]

This paper deals with lack-of-knowledge uncertainty in complex non-linear simulations on a component level, i.e., a crashbox during frontal impact of a vehicle. Specifically, the focus lies on using interval field techniques to model the uncertain boundary conditions during impact simulations. The uncertainty considered in this work is the unknown mechanical response from the adjacent structure. This uncertainty is considered to be epistemic, representing the case where this adjacent structure is unknown at the time the impact analysis is performed. In practice, this refers to the situation where the adjacent structure is still under development, e.g., at a different department or even outsourced. In addition, the safety critical performance of both, the component and the overall structure should be guaranteed under a wide range of circumstances, which are typically encountered in real-life situations. Typically, car manufacturers use multidisciplinary optimisation to identify component designs that perform best on all requirements in a deterministic sense, while minimising the overall weight. Unfortunately, the results of such optimisation schemes are known to converge to an often non-robust optimum. As a result, the response of the structure may be sensitive to small changes in input parameters or boundary conditions. As an answer to these challenges, this paper proposes an interval field approach that accounts for the epistemic, i.e., lack-of-knowledge, uncertainty of the adjacent structures, even in an early design stage. This is accomplished by introducing a spatially varying uncertain mechanical compliance in elements that connect the component to the adjacent structures. These elements have an interval valued stiffness, which is varied along the component following the realisations of an interval field. The bounds on the interval-valued response quantities of interest, i.e., mean force and peak force, are identified using a differential evolution algorithm. This method is demonstrated on four case studies of a full overlap crash analysis of a rectangular crash box, which represents a generic component within the front structure of a vehicle. These case studies demonstrate the applicability and the potential of the proposed method. In addition, in the last case it is shown that the performance of the component can be assessed under an increasing range of uncertainty. • A novel application of interval fields in the domain of crash analysis. • The method considers impact performance assignment under uncertain adjacent structures. • This paper presents a novel way of designing safety critical structures. • The method is applied and demonstrated on four case studies. [ABSTRACT FROM AUTHOR]

Abstract: In construction industry, cost escalation is a major cause of project failure. The reason is that construction prices fluctuate over time and they are influenced by numerous factors. This study establishes a hybrid intelligence system, named as ELSVM, for modeling construction price variations quantified by the construction cost index (CCI). The proposed method is developed based on the fusion of Least Squares Support Vector Machine (LS-SVM) and Differential Evolution (DE). LS-SVM is used for mining the mapping function between CCI and its causative input factors. Meanwhile, DE is employed to optimize LS-SVM tuning parameters. In this research, a database consisting of 122 historical cases was collected to establish the intelligence system. Based on experimental results, ELSVM can successfully model CCI fluctuations since it has achieved a comparatively low MAPE which is less than 1%. Thus, the proposed system can be a promising tool to assist decision-makers in construction management. [Copyright &y& Elsevier]

Abstract: This paper presents how an algorithm based on Differential Evolution (DE) with no constant control parameters solves the dimensional synthesis of four and six-bar mechanisms for path generation. The selection of values of DE control parameters is not always an easy and obvious job. In this paper a DE with auto-adaptive control parameters is proposed which includes a new mutation operator to solve stagnation in local minima and, on top of all this, the choice of a control parameters value in a simple way. For a set of 6 representative cases related to dimensional synthesis from bibliography, the performance of this new DE algorithm, called Ingeniería Mecánica Málaga (IMMa) Optimization Algorithm with Self-Adaptive Technique, IOAs-at, has been tested. An explanation of how the new mutation operator works is also included. The results obtained, compared with other synthesis techniques from literature, present significant improvements for every case studied. The new version does not require control input parameters to be chosen by the user. Those are auto-tuned during the algorithm execution, that is, we use an algorithm with adaptive control parameters and it continues being easy-to-use, robust and fast. [Copyright &y& Elsevier]

Differential evolution (DE) is a simple but powerful evolutionary optimization algorithm with continually outperforming many of the already existing stochastic and direct search global optimization techniques. DE algorithm is a new optimization method that can handle non-differentiable, nonlinear, and multimodal objective functions. This paper presents an efficient modified differential evolution algorithm for solving economic dispatch problem. A new mutation strategy of the conventional DE is suggested to improve the performance and avoid premature convergence. Numerical results on the IEEE 30 bus test system and the practical Algerian 59 bus system show that the proposed approach is faster and more robust compared with those reported recently in the literature. The comparison results prove the capability of the proposed method in real-time implementation for the economic dispatch problem. [ABSTRACT FROM AUTHOR]

Abstract: The real world contains many sources of natural variation. Useful simulation models of real-world problems, such as the optimal allocation of beef cattle into feedlot pens, need to take this into account. Monte Carlo simulation is the usual method of achieving this, using multiple model runs, each with a different set of randomly generated variables. However, when the goal is system optimisation, this approach can make the computation extremely intensive, and difficult for the optimisation of even moderately-sized models. This problem was addressed in an industry model of feedlot operations, where animals are selected and drafted into separate pens, and grown to the optimal weight and fat ranges in the pricing grid. For each pen, it is planned that the majority of the animals achieve this targeted region of the price grid, so that the number of discounted carcasses will be minimal. The main problem is that animals which appear reasonably similar at intake then display variable growth and fattening rates. Hence deterministic predictions are unrealistic, and stochasticity needs to be factored in. However this interferes with the efficient optimisation of this feedlot operation, which ultimately needs to be done ‘real-time’ (at animal induction), and on-site at the feedlot. Our solution to this dilemma was to transfer all of the modelled stochastic processes onto the evaluation side of this problem, by integrating over Normal distributions applied to the expected prices for each animal. This allows the optimisation runs to directly estimate the expected economic outcome using just one model evaluation for each trial management strategy. This process is illustrated with two case-studies, based on separate past intakes of animals into a commercial feedlot. For both intakes, the simulated results as reflected by the carcass values for the actual times on feed matched well with the observed values. Simulated results indicate that increases in profitability can be obtained both by altering the days on feed, and by better allocation of the animals to the pens. The overall predicted increase in profit per animal was $34 for the first intake, where the actual allocation of animals to the pens was approximately random (the order they were offloaded), and $20 for the second example where the animals had been allocated to pens on a more structured basis (by weights). This optimised allocation scheme appears to offer obvious improvements for feedlot operations, and is currently being trialed by a number of industry collaborators. [Copyright &y& Elsevier]

The biologically inspired concept of hidden genes has been recently introduced in genetic algorithms to solve optimization problems where the number of design variables is variable. In multigravity-assist trajectories, the hidden genes genetic algorithms demonstrated success in searching for the optimal number of swing-bys and the optimal number of deep space maneuvers. Previous investigations in the literature for multigravity-assist trajectory planning problems show that the standard differential evolution is more effective than the standard genetic algorithms. This paper extends the concept of hidden genes to differential evolution. The hidden genes differential evolution is implemented in optimizingmultigravity-assist space trajectories. Case studies are conducted, and comparisons to the hidden genes genetic algorithms are presented in this paper. [ABSTRACT FROM AUTHOR]

Abstract: Many of the proposed approaches for non-linear systems control are developed under the assumption that all involved parameters are known in advance. Unfortunately, their estimation is not so simple because the nature of the non-linear behaviors is very complex in the most part of the cases. In view of this complication, parameters identification of non-linear oscillators has attracted increasing interests in various research fields: from a pure mathematical point-of-view, parameters identification can be formalized as a multi-dimensional optimization problem, typically over real bounded domains. In doing this, the use of the so-called non-classical methods based on soft computing theories seems to be promising because they do not require a priori information and the robustness of the identification against the noise contamination is satisfactory. However, further studies are required to evaluate the general effectiveness of these methodologies. In this sense, the paper addresses the consistency of two classes of soft computing based methods for the identification of Van der Pol–Duffing oscillators. A large numerical investigation has been conducted to evaluate the performances of six differential evolution algorithms (including a modified differential evolution algorithm proposed by the authors) and four swarm intelligence based algorithms (including a chaotic particle swarm optimization algorithm). Single well, double well and double-hump oscillators are identified and noisy system responses are considered in order to evaluate the robustness of the identification processes. The investigated soft computing techniques behave very well and thus they are suitable for practical applications. [Copyright &y& Elsevier]

Reservoir flood control operation (RFCO) is a complex problem because it needs to consider multiple objectives and a large number of constraints. Traditional methods usually convert multiple objectives into a single objective to solve, using weighted methods or constrained methods. In this paper, a new approach named multi-objective cultured differential evolution (MOCDE) is proposed to deal with RFCO. MOCDE takes cultural algorithm as its framework and adopts differential evolution (DE) in its population space. Considering the features of DE and multi-objective optimization, three knowledge structures are defined in belief space to improve the searching efficiency of MOCDE. MOCDE is first tested on several benchmark problems and compared with some well known multi-objective optimization algorithms. On achieving satisfactory performance for test problems, MOCDE is applied to a case study of RFCO. It is found that MOCDE provides decision makers many alternative non-dominated schemes with uniform coverage and convergence to true Pareto optimal solutions in a short time. The results obtained show that MOCDE can be a viable alternative for generating optimal trade-offs in reservoir multi-objective flood control operation. [ABSTRACT FROM AUTHOR]

This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher. [ABSTRACT FROM AUTHOR]

• Multi-population mechanism is introduced into salp swarm algorithm. • The proposed method is abbreviated as MCSSA. • Chaos-assisted exploitation strategy is introduced into MCSSA. • MCSSA is tested over 30 benchmark functions and 5 real-world problems. • The greater effectiveness of MCSSA is confirmed over the advanced algorithms. Salp swarm algorithm (SSA) is a recently presented algorithm, which is simple in structure and relatively mediocre in its performance. However, the original SSA still has features to be improved because it may face problems in convergence trends or easily being trapped into local optima for more advanced problems. To alleviate this limitation, we propose a new SSA-based method (MCSSA) that performs the chaotic exploitative trends and has a multi-population structure. The new structure can assist SSA in making a more stable tradeoff between global exploration and local exploitation capabilities. First, the exploitation trends and neighborhood searching commands of SSA are enriched using the chaos-assisted exploitation strategy. Next, we arrange a multi-population structure with three sub-strategies to augment the global exploration capabilities of the algorithm. To test the performance of this proposed MCSSA, a set of comprehensive algorithms is used, including 11 other original methods, conventional SSA, and 13 advanced techniques including SCA, SSA, GWO, MFO, WOA, BA, FPA, PSO, ALO, MVO, DE, ABC, CSSA, ESSA, CLSGMFO, LGCMFO, SaDE, jDE, EPSO, ALCPSO, CBA, RCBA, BWOA, CCMWOA, and GA-MPC based on 30 IEEE CEC2017 benchmark functions and 5 IEEE CEC2011 practical test problems. Also, the non-parametric statistics Wilcoxon signed-rank test and Friedman test are also used as an enabling tool to validate the performance of the proposed algorithm. From the result analysis, it can be concluded that the introduced strategy significantly improves the speed of the algorithm converging to the optimal value, and the improvement of the search ability also helps the algorithm to find a better solution than the basic SSA. As a conclusion, it can be said that MCSSA is reliable and efficient in solving complex optimization problems. An online website at https://aliasgharheidari.com supports this research for any guide or info. [ABSTRACT FROM AUTHOR]

It is usually very difficult to elicit the parameter values of models representing decision makers' preferences. Consequently, some imprecision, ill-determination and arbitrariness are unavoidable. Moreover, such elicitation cannot be performed by traditional optimization techniques in a reasonable time. Therefore, we present here a novel elicitation method guided by a genetic algorithm whose main contribution is coping with imperfect knowledge. The latter is done by using interval numbers representing all the possible values that the parameters can attain. The assessment of the method showed its high ability to reproduce the decision maker's preferences. Finally, as the method proposed in this paper is the complement of the authors' previous work regarding the optimization of stock portfolios, we provide a case study in such a field. We use differential evolution to obtain the most satisfactory portfolio. The results reported here show that the best portfolio returns are obtained when the elicitation method is exploited, and we conclude that the new overall approach might be an interesting alternative to the already-existing methods. • We present a novel method to infer the parameters of decision makers' systems of preferences using evolutionary algorithms. • The method's main contribution is coping with imperfect knowledge on the decision maker's preferences and criterion scores. • An application in an extensive case study on optimization of stock portfolios is provided. • The results support the convenience of the proposed method in situations of uncertainty over all the benchmarks used. [ABSTRACT FROM AUTHOR]