Chaotic Differential Evolution Algorithm Research Articles

A power generation accumulation-based adaptive chaotic differential evolution algorithm for wind turbine placement problems

A power generation accumulation-based adaptive chaotic differential evolution algorithm for wind turbine placement problems

Iterated-local-search-based chaotic differential evolution algorithm for hybrid-load part feeding scheduling optimization in mixed-model assembly lines

PurposeDriven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted.Design/methodology/approachA novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy.FindingsThe CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod.Originality/valueThis study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production.

Triple-layered chaotic differential evolution algorithm for layout optimization of offshore wave energy converters

Renewable energy sources are progressively assuming a pivotal role in shaping our future, and wave energy stands out as a promising avenue due to its substantial potential and minimal ecological impact. Consequently, extensive research has been directed towards optimizing the layout of wave energy converters (WECs). However, as the number of optimized buoys increases, so does the complexity of calculating hydrodynamic interactions, placing great demands on computing power. Simultaneously, these dynamic interactions can yield either constructive or detrimental outcomes, amplifying the intricacy of layout evaluation. Effectively and promptly determining the optimal buoy arrangement to achieve high energy output efficiency emerges as a pivotal research challenge. We propose a chaos-based differential evolutionary algorithm with a three-layer information structure, including excavation, balancing, and recycling layers, which reasonably adjusts the population structure and makes full use of individual information to optimize the next exploration by combining with chaotic maps. We compared our method with other state-of-the-art intelligent algorithms applied to the problem of wave energy generators and tested it in four real wave scenarios (Perth, Adelaide, Tasmania, and Sydney) using numerical modeling to calculate its energy output. The experimental results show that our improved strategy improves the energy output of the oscillating buoy-type wave energy generator by an average of 101.5%, 93.1%, 23.1%, and 0.7% compared to the mainstream state-of-the-art algorithm for the four scenarios, respectively.

An exponential chaotic differential evolution algorithm for optimizing bridge maintenance plans

Bridges are one of the fundamental infrastructure assets that are vital for economic growth and public welfare. Over the past few decades, the numbers of deteriorating bridges have drastically escalated raising concerns for serviceable, safe and functional transportation networks. This state of affairs poses a paramount challenge especially when coupled with the need to address social and environmental constraints. Accordingly, this current research paper proposes an automated three-component model for bridge maintenance optimization at both project and network levels. The first component aims at identifying the physical characteristics of the tackled bridge inventory. The second component encompasses designing a multi-objective optimization model to determine the optimal set of maintenance plans through four principal objective functions. These functions comprise maximization of performance condition of bridge elements, minimization of agency and user costs, minimization of duration of traffic disruption and minimization of environmental impact. In the multi-objective optimization model, an exponential chaotic differential evolution (ECDE) algorithm is introduced in an attempt to circumvent the drawbacks of convergence speed and search behavior of classical meta-heuristics. The third component combines criteria importance through inter-criteria correlation (CRITIC), complex proportional assessment (COPRAS) and grey relational analysis (GRA) to select the most optimum maintenance plan for each study period. Comparison results revealed that ECDE-based Sinusoidal algorithm managed to improve the performance diagnostics of classical meta-heuristics by values ranged from 49.2% to 73.1% over the multi-year maintenance plans. The results of benchmark test functions exemplified that ECDE-based Sinusoidal algorithm performed better than genetic and differential evolution algorithms by 114.2% and 79.5%, respectively. The developed integrated model is expected to assist infrastructure managers in executing optimized and sustainable maintenance budget plans within various planning scenarios.

Load mitigation of a class of 5-MW wind turbine with RBF neural network based fractional-order PID controller

In variable-pitch wind turbines, pitch angle control is implemented to regulate the rotor speed and power production. However, mechanical loads of the wind turbines are affected by the pitch angle adjustment. To improve the performance and at the same time alleviate the mechanical loads, a gain-scheduling fractional-order PID (FOPID), where a trained RBF neural network chooses its parameters is proposed. The database, which the RBF neural network is trained based on, is created via optimization of a FOPID in several wind speeds with chaotic differential evolution (CDE) algorithm. The simulation results are compared to an RBF based PID controller that is designed via the same method, a conventional gain-scheduling baseline PI controller developed by NREL, an optimal RBF based PI controller, and a FOPI controller. The simulations indicate that the RBF based FOPID improves the control performance of the benchmark wind turbine in comparison to the other controllers, while the applied loads to the structure are mitigated. To validate the performance and robustness, all controllers are implemented on FAST wind turbine simulator. The superiority of the proposed FOPID controller is depicted in comparison to the other controllers.

Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis

Placement of optimally sized distributed generator (DG) units at optimal locations in the radial distribution networks can play a major role in improving the system performance. The maximum economic and technical benefits can be extracted by minimizing various objectives including yearly economic loss which includes installation, operation and maintenance cost, power loss as well as voltage deviation throughout the buses. The present problem is analysed considering these multi-objective frameworks and presents the best compromise solution or Pareto-optimal solution. Several equality and inequality constraints are also considered for the multi-objective optimization problem. In this paper, a novel multi-objective opposition based chaotic differential evolution (MOCDE) algorithm is proposed for solving the multi-objective problem in order to avoid premature convergence. Performance of population based meta-heuristic techniques largely depends on the proper selections of control parameters. It is reported that wrong parameters selection may lead to premature convergence and even stagnation. The proposed technique uses logistic mapping to generate chaotic sequence for control parameters. The proposed algorithm is implemented on IEEE-33 and IEEE-69 bus radial distribution systems for verifying its effectiveness. A comparative analysis with other modern multi-objective algorithms like NSGA-II, SPEA2 and MOPSO is also presented in this work. It is observed that the proposed algorithm can produce better results in terms of power loss and yearly economic loss minimization as well as improvement of voltage profile.

Non-interactive approach to solve multi-objective thermal power dispatch problem using composite search algorithm

This paper deals with a non-interactive approach to solve multi-objective thermal power load dispatch (MTPLD), where either decision maker is not involved or preference information is available in prior. To reduce the computational complexities due to generation of Pareto-front and selection of satisficing solution, this paper adopts no-preference approach. A satisficing function to resolve the conflict of non-commensurable objectives is proposed, which reformulates the MTPLD problem as scalar thermal power load dispatch (MTPLD) problem. Owing to ambiguous or vague in objectives, the proposed method exploits fuzzy theory. MTPLD problems’ satisfying solution is obtained by implementing hybrid chaotic differential evolution algorithm and Powell’s pattern search algorithm (CDEPS). The chaotic differential evolution algorithm is responsible for the diversification of feasible solutions and provides global solution. Whereas Powell’s pattern search, method improves the exploitation by performing local search. The paper investigates the performance of two CDEPS variants based on Gauss map and Tent map, respectively. The performance of the proposed solution procedure is analyzed using generalized benchmark test functions and complex MTPLD problems. The exhaustive analysis using non-parametric significance test and descriptive statistics shows that the Tent map based CDEPS solution procedure has better ability to generate quality generation schedule and faster convergence rate.

Reactive Power Optimization Using Hybrid CABC-DE Algorithm

Reactive power optimization is closely related to voltage quality and network loss, and it has great significance for the safety, reliability, and economical operation of the power system. Differential evolution (DE) algorithm has been currently applied to reactive power optimization. In order to mitigate the shortcomings of poor local search ability and premature convergence in DE, this paper presents a novel hybrid algorithm–chaotic artificial bee colony differential evolution (CABC-DE) algorithm, which improves the DE algorithm based on artificial bee colony algorithm and ideas of chaotic search. It introduces the observation bees' acceleration operation and the detective bees' chaotic search operation into CABC-DE. The validity of the proposed method is examined using IEEE-14 and IEEE-30 bus system. The experimental results show that CABC-DE algorithm is more effective than regular DE algorithm for reactive power optimization. The algorithm can save the search time greatly and get a better solution for optimization, thus making it suitable for solving reactive power optimization problems.

Pattern synthesis of MIMO radar based on chaotic differential evolution algorithm

A Chaotic Differential Evolution (CDE) Algorithm based method for pattern synthesis of MIMO radar is proposed. By optimizing positions and excitation amplitude of the element in receive and transmit array, better control of side lobe level and null depth can be achieved. CDE which is proposed by introducing Chaotic Optimization (CO) mechanism to Differential Evolution (DE), the risk of getting local optimal position can be reduced, thus the premature of DE can be avoid, and the performance of global optimization is improved. Simulation results validate the effectiveness of the proposed method and superiorities.

Prediction and optimal allocation of agricultural non-point source pollution based on chaos theory

In order to promote the accuracy of agricultural pollution source prediction algorithm, an agricultural pollution source prediction algorithm based on chaotic differential evolution algorithm neural network is proposed in the article. Firstly, it initialises the population of differential evolution algorithm with chaos theory, to promote the diversity for initial population solution; then it improves the differential evolution algorithm by using mean entropy and perturbation variation method to promote its optimise performance. Secondly, it optimises the neural network parameter learning process by using the improved differential evolution algorithm to promote the accuracy of parameters optimisation. Lastly, it applies the proposed algorithm into the example of local agricultural pollution source prediction and the results showed that the proposed method could effectively increase the accuracy of agricultural pollution source prediction.

Speech Recognition Based on Fuzzy Neural Network and Chaotic Differential Evolution Algorithm

Speech Recognition Based on Fuzzy Neural Network and Chaotic Differential Evolution Algorithm

A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

One of the main limitation of the application of evolutionary algorithms (EA) is the tendency to converge prematurely to a local optimum. The EAs suffer with the disadvantage of premature convergence and hence the study on convergence of EAs is always one of the most important research fields. Due to outstanding capability of chaos to avoid being trapped in local optimum, it can be considered as an efficient search tool. Therefore, in current paper, in order to taking properties of chaos, eight chaotic maps are employed within a differential evolution (DE) algorithm for solving a stochastic job scheduling problem. To speedup searching and avoid local optimum traps, the random sequences produced from chaotic maps are utilized instead of random variables in DE. Furthermore, to address the uncertainties arising in scheduling environments, Monte Carlo simulation is used. However, simulation is not an optimization approach. Therefore, we design the simulation-based optimization approach where a simulator is combined with chaotic DE. The simulation experiments are used to evaluate the quality of candidate solutions and the chaotic DE is utilized to find best-compromised solutions and then guide the search direction. The performance of simulation-based chaotic DE algorithm is investigated in a computational study, and the results show the outperformance of suggested method with respect to the traditional methods.

A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling

The short-term optimal hydrothermal scheduling (STOHS) plays one of the most important roles in power systems operation. The STOHS problem involves the solution of difficult constrained optimization problems that require good computational techniques. This paper proposes a modified chaotic differential evolution (MCDE) approach for the solution of this difficult optimization problem. A repair strategy and a novel selection operation are simultaneously introduced into the MCDE approach for handling constraints of the problem. The repair strategy preserves the feasibility of solutions generated and avoids the use of penalty factors as much as possible. The introduced selection operation makes a not clearly distinction between feasible solutions and infeasible ones at early stage of the algorithm and makes a clearly distinction at the later stage. Additionally, an adaptive regeneration operation is proposed to enhance population diversity and to avoid local optimums. Moreover, a chaotic local search technique is introduced also to accelerate the searching process of the algorithm. The proposed MCDE approach is applied to three well-known hydrothermal test systems in order to verify its feasibility and efficiency. The obtained results are compared with those obtained by other population-based heuristic approaches reported in literature. It is observed from the comparisons that the proposed MCDE approach performs effectively and can yield competitive solutions.

A novel chaotic differential evolution algorithm for short-term cascaded hydroelectric system scheduling

A novel chaotic differential evolution (CDE) algorithm of optimal scheduling of short-term cascaded hydroelectric system based on improved logistic map is presented to maximize the expected generation benefit in a day, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of expected power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of differential evolution (DE) algorithm. The improved logistic map between (−1,1) is utilized to explore globally around the best individual until the lagged ones are close to best one. Meanwhile, the fitness value of objective function is handled by a piecewise linear interpolation function (PLIF). The new hybrid method has been examined and tested on a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CDE algorithm are better than existing algorithms.

A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization

Evolutionary algorithms (EAs) have yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization problems. Due to its population-based nature, EAs can avoid being trapped in a local optimum, and consequently have the ability to find global optimal solutions. As a novel evolutionary technique, differential evolution (DE) has received increasing attention and wide applications in a variety of fields. DE algorithm uses an efficient way of self-adapting mutation using small populations for function optimization over continuous spaces. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution, and robustness. In this paper, an effective self-adaptive DE algorithm based on Gaussian probability distribution, gamma distribution and chaotic sequence (DEGC) for solving continuous global optimization problems is proposed. The proposed DEGC algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with a classical DE approach and a self-adaptive DE approach demonstrate the effectiveness and efficiency of the proposed DEGC algorithm.

Chaotic differential evolution algorithm for resource constrained project scheduling problem

In this paper, a new optimisation method based on chaotic differential evolution CDE algorithm for solving resource-constraint project scheduling problem RCPSP is proposed. By using improved logistic chaotic map and penalty function, the objective function can be solved more perfectly. Project scheduling problem library PSPLIB has been taken into account so as to test the effect of novel hybrid method. Simulation results and comparisons show that the chaotic differential evolution algorithm is competitive and stable in performance with other optimisation approaches presented in literatures.

System modeling and optimization for islanded micro-grid using multi-cross learning-based chaotic differential evolution algorithm

This paper presents a comprehensive operation model for micro-grids (MG) operating in the islanded mode. Various energy sources of a MG including diesel engine generator, micro-turbine, wind turbine and photovoltaic cell as well as battery storage and AC/DC rectifier/inverter are modeled in the proposed framework. Fuel costs, emission costs, and operation and maintenance (O&M) costs of these sources as well as their operating limits and characteristics are considered in the model. Furthermore, a new multi-cross learning-based chaotic differential evolution (MLCDE) algorithm is presented to solve the optimization problem of MG operation. The numerical results obtained from the proposed solution approach for three MG test cases with real-world data are compared with the results of several other recently published optimization methods. These comparisons confirm the validity of the developed approach.

Coordination of directional overcurrent relays using opposition based chaotic differential evolution algorithm

Coordination of directional over current relays (DOCR) has been considered as an optimization problem and has been solved using different types of optimization techniques by the developers. A wide variety of algorithms have been proposed over the past few decades for dealing with such problem. However considering its practical significance, there is always a scope for improvement in this area to get better results. In the present study, a set of two algorithms named opposition based chaotic differential evolution (OCDE1 and OCDE2) has been developed and applied to solve this problem. Both variants use the concept of opposition based learning and chaotic scale factor for obtaining an optimum solution. The proposed method is implemented in four test cases. The results are compared with previously proposed algorithms available in the literature and it was observed that the proposed schemes are quite competent for dealing with such problems.

Assessment on Flood Disaster Based on Projection Pursuit Model and Chaotic Differential Evolution Algorithm

In allusion to the problem of flood disaster classification, this paper proposes a chaotic differential evolution algorithm for to calculate the best projection direction for a projection pursuit model. By using polynomial function and penalty function, the objective function can be solved more perfectly. The new algorithm has been examined and tested with a real flood disaster.

Chaotic Differential Evolution Algorithm Based On Competitive Coevolution And Its Application To Dynamic Optimization Of Chemical Processes

A chaotic differential evolution algorithm based on competition coevolution is proposed to improve the performance of the differential evolution (DE) algorithm. In the proposed algorithm (named CO-CDE), at first the population is divided into several sub-populations, each sub-population evolves individually, using different differential schemes. At the end of the evolution each sub-population will have one individual with best fitness. After that all the individuals with best fitness compete with each other, at this time the fitness of one individual is defined as the number of times one individual is superior to others. Therefore one individual with best fitness is picked out and its information is shared by the whole population. To avoid premature convergence and raise the probability of escaping from local optima, a chaotic evolutionary operation based on chaotic variables is introduced into the algorithm and implemented to the whole population. The simulation experiment shows that the CO-CDE algorithm...