Adaptive Differential Evolution Algorithm Research Articles

Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution

Differential evolution (DE) is an intelligent optimization algorithm inspired by biological evolution. Setting a mutation strategy and control parameters that meet the optimization requirements are the premise for DE to achieve good performance. This paper proposes a parameter and strategy adaptive differential evolution algorithm based on accompanying evolution (APSDE). Through the accompanying population, in which individuals are composed of suboptimal solutions, the mutation strategy and control parameters are optimized to realize the adaptation of the strategy and parameters of the main population. Population diversity is enhanced in evolution by generating reverse individuals. In addition, radial spatial projection technology is utilized to track the change in evolution direction with optimization. The performance of APSDE is validated under four sets of benchmark problem suites from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC), and compared with state-of-the-art optimization algorithms. The results show that the proposed algorithm has better optimization performance than the competitive algorithms because of its efficient adaptive mechanism and its excellent population diversity.

Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and Cr values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems.

Scheduling Optimization of Multiple Hybrid-Propulsive Spacecraft for Geostationary Space Debris Removal Missions

To efficiently implement space debris removal missions on geostationary Earth orbit (GEO), a scheduling optimization scheme of multiple hybrid-propulsive servicing spacecraft (SSc) is proposed. In this scheme, a many-to-many task allocation coding model is established to formulate the removal sequences of debris targets with respect to different SSc. And a hybrid-propulsive orbit transfer strategy is developed to implement the debris removal maneuvers to save the propellant consumption and removal time simultaneously via using both electric and chemical propulsion systems. Then, the multiple GEO space debris removal scheduling optimization problem is formulated to minimize the total propellant consumption by exploring the optimal removal sequences and orbit maneuver parameters, subject to the available velocity increment, removal time, and many-to-many task allocation constraints. To effectively solve the scheduling optimization problem involving discrete-continuous mixed variables, a clustering based adaptive differential evolution algorithm with potential individual reservation (CADE-PIR) is proposed. In CADE-PIR, the k-means clustering method and potential individual reservation mutation strategy are employed to promote the convergence rate and performance of exploration and exploitation. In the end, a real-world GEO space debris removal mission with multiple SSc is investigated to demonstrate the effectiveness and practicality of the proposed optimization scheme.

Coordinated Design of Type-2 Fuzzy Lead–Lag-Structured SSSCs and PSSs for Power System Stability Improvement

This work suggests a type-2 fuzzy lead–lag (T2FLL) controller structure for flexible AC transmission system (FACTS)-based damping controllers and power system stabilizers (PSSs) for power system stability improvement. The values of the suggested controller are optimized by a hybrid adaptive differential evolution and pattern search algorithm (hADE-PS) method. Initially, a single-machine infinite-bus (SMIB) system with lead–lag (LL)-structured FACTS and PSS controllers is considered, and the dominance of the hADE-PS method is established over the original differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The supremacy of T2FLL over the lead–lag (LL) controller is established under different large and small disturbance conditions, as well as varied loading conditions and fault positions. Lastly, the effectiveness of T2FLL is evaluated in a multimachine power system (MMPS). It is demonstrated that the suggested T2FLL offers better performance than the LL controller under various large and small disturbance conditions by providing significantly more damping to all modes of oscillations.

Parameters Identification of Battery Model Using a Novel Differential Evolution Algorithm Variant

In order to deal with the fluctuation and intermittency of photovoltaic (PV) cells, the battery energy storage system (BESS) as a supplementary power source has been widely concerned. In BESS, the unknown parameters of the battery can affect its output, and its structure determines these parameters. Therefore, it is essential to establish the battery model and extract the parameters accurately, and the existing methods cannot effectively solve this problem. This study proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOLADE) to deal with the issue. In DOLADE, the number of elite particles and particles with poor performance is expanded, the population’s search area is increased, and the population’s exploration capability is improved. The particles’ search area is dynamically changed to ensure the population has a good exploitation capability. The dynamic opposite learning (DOL) strategy increases the population’s diversity and improves the probability of obtaining the global optimum with a considerable convergence rate. The various discharging experiments are performed, the battery model parameters are identified, and the results are compared with the existing well-established algorithms. The comprehensive results indicate that DOLADE has excellent performance and could deal with similar problems.

Placement Optimization for Multi-IRS-Aided Wireless Communications: An Adaptive Differential Evolution Algorithm

Using intelligent reflecting surfaces (IRSs) is a promising approach to enhance the performance of wireless communication systems. In this letter, the placement optimization of multi-IRSs is investigated in multi-IRS-aided wireless communication systems, with the aim of minimizing the number of IRSs subject to the average achievable data rate. Then, an adaptive differential evolution algorithm is developed to jointly optimize the number, locations, and phase shift coefficients of IRSs, in which a novel strategy is devised to adaptively select the mutation operator for each individual. Compared with other algorithms, the proposed algorithm performs well in reducing the number of IRSs while satisfying the average achievable data rate.

Adaptive Differential Evolution Algorithm Based on Fitness Landscape Characteristic

Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for different fitness landscape conditions to find the optimal solution, and there is not a single strategy that can be suitable for all fitness landscapes. As a result, developing a strategy to adaptively steer population evolution based on fitness landscape is critical. Motivated by this fact, in this paper, a novel adaptive DE based on fitness landscape (FL-ADE) is proposed, which utilizes the local fitness landscape characteristics in each generation population to (1) adjust the population size adaptively; (2) generate DE/current-to-pcbest mutation strategy. The adaptive mechanism is based on local fitness landscape characteristics of the population and enables to decrease or increase the population size during the search. Due to the adaptive adjustment of population size for different fitness landscapes and evolutionary processes, computational resources can be rationally assigned at different evolutionary stages to satisfy diverse requirements of different fitness landscapes. Besides, the DE/current-to-pcbest mutation strategy, which randomly chooses one of the top p% individuals from the archive cbest of local optimal individuals to be the pcbest, is also an adaptive strategy based on fitness landscape characteristic. Using the individuals that are approximated as local optimums increases the algorithm’s ability to explore complex multimodal functions and avoids stagnation due to the use of individuals with good fitness values. Experiments are conducted on CEC2014 benchmark test suit to demonstrate the performance of the proposed FL-ADE algorithm, and the results show that the proposed FL-ADE algorithm performs better than the other seven highly performing state-of-art DE variants, even the winner of the CEC2014 and CEC2017. In addition, the effectiveness of the adaptive population mechanism and DE/current-to-pcbest mutation strategy based on landscape fitness proposed in this paper are respectively verified.

Adaptive Differential Evolution Algorithm with Simulated Annealing for Security of IoT Ecosystems

With the wide application of the Internet of Things (IoT) in real world, the impact of the security on its development is becoming incrementally important. Recently, many advanced technologies, such as artificial intelligence (AI), computational intelligence (CI), and deep learning method, have been applied in different security applications. In intrusion detection system (IDS) of IoT, this paper developed an adaptive differential evolution based on simulated annealing algorithm (ASADE) to deal with the feature selection problems. The mutation, crossover, and selection processes of the self-adaptive DE algorithm are modified to avoid trapping in the local optimal solution. In the mutation process, the mutation factor is changed based on the hyperbolic tangent function curve. A linear function with generation is incorporated into the crossover operation to control the crossover factor. In the selection process, this paper adopts the Metropolis criterion of the SA algorithm to accept poor solution as optimal solution. To test the performance of the proposed algorithm, numerical experiments were performed on 29 benchmark functions from the CEC2017 and six typical benchmark functions. The experimental results indicate that the proposed algorithm is superior to the other four algorithms.

Analysis and optimal control of smart damping for porous functionally graded magneto-electro-elastic plate using smoothed FEM and metaheuristic algorithm

This paper proposed an effective numerical approach for free vibration analysis and optimal control of the porous functionally graded magneto-electro-elastic (PFGMEE) plates integrated with the active constrained layer damping (ACLD). Specifically, in order to analyze the free vibration characteristic of the PFGMEE plates based on the layer-wise shear deformation theory, a cell-based smoothed discrete shear gap (CS-DSG3) method is applied. The accuracy and convergence of the CS-DSG3 method are demonstrated by comparing its results with those available in the literature. Additionally, due to the coupling characteristic between magnetic, electric, and elastic fields, the coupled response of the PFGMEE plate is also considered. Furthermore, in order to obtain the optimal performance of the PFGMEE plates with the ACLD treatment, a combination of the CS-DSG3 method with an adaptive elitist differential evolution (aeDE) algorithm is used to determine the optimal placement of ACLD patches and the piezoelectric fiber orientation angle of the PFGMEE plates. The effect of several boundary conditions and porous distribution types on the free vibration response and the optimal control solution of the PFGMEE plates is also examined.

Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning

The performance of photovoltaic (PV) cell is affected by the model structure and corresponding parameters. However, these parameters are adjustable and variable, which play an available role in regarding to the efficiency and effectiveness of PV generation. Due to strong non-linear characteristics, existing PV model parameters identification methods cannot easily obtain accurate solutions. To tackle this, this paper proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOL), named DOLADE. In DOLADE, the opposite learning method expands the current elite population and the population of poor performance, improving the particles’ exploration capability. In the process of particles work, the searching area of particles is adjusting dynamically so that the particles’ exploitation capability is enhanced. The experimental data of different types of PV are tested, respectively. Three PV models are used to verify the new strategy’s accuracy and effectiveness. The proposed DOLADE is compared with several general advanced algorithms, and comprehensive experimental results are demonstrated. The results illustrate that DOLADE well extracts optimal parameters for each PV cell model and brought great competition in terms of accuracy, reliability, and computational efficiency in solving the problem.

Improved MOSADE algorithm incorporating Sobol sequences for multi-objective design of Water Distribution Networks

Design of Water Distribution Networks (WDNs) is a tremendously hard optimization problem, and consideration of reliability further adds to the complexity, which may involve huge computational effort. As several past studies stressed that Evolutionary Algorithms (EAs) could be efficient tools for WDN design, so this study, presents an effective methodology, Multi-Objective Self Adaptive Differential Evolution (MOSADE) algorithm using Sobol sequences for random number generation, termed as (S-MOSADE) for WDN design. The efficacy of the S-MOSADE framework is evaluated by application on a few benchmark WDNs, by considering cost minimization and mechanical reliability maximization and comparing the results with that of NSGA-II algorithm. The results illustrated that S-MOSADE algorithm leads to a better Pareto-optimal front than NSGA-II with respect to uniformly spaced and wide range of non-dominated solutions, and converges faster as compared to other algorithms. To further reduce the computational burden, minimizing the cost and maximizing the network resilience is carried out to generate the initial population for S-MOSADE algorithm. This has reduced the computational burden by almost three times as compared to random initialization of population, thus saving a lot of computational time. The study concludes that the proposed S-MOSADE algorithm with the strategy of solutions initialized with minimum cost and maximum network resilience could be used effectively for speeding-up the multi-objective design of WDNs.

Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm

Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm

Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.

A novel adaptive 3-stage hybrid teaching-based differential evolution algorithm for frequency-constrained truss designs

This study proposes an adaptive 3-stage hybrid teaching-based differential evolution (ATDE) algorithm to deal with sizing and layout frequency-constrained truss designs more efficiently. The optimization process is divided into 3 stages, and suitable mutation formulae are applied adaptively in each stage. A novel operator inspired by the teaching phase of teaching learning based optimization (TLBO) is introduced in the early stage with a modified ‘DE/rand/1’, and two other adjusted DE formulae are appropriately executed in later stages to provide a straightforward yet better exploration-exploitation-balanced mutation scheme. A gradually-shrinking mechanism is suggested to make the mutations parameter-free as well as improve the searching ability. In addition, as the enhanced mutation phase is more likely to generate promising mutants, an elitist selection technique allowing only best individuals to survive is employed to accelerate the evolution process. Various weight minimization benchmark truss examples are performed to validate the effectiveness and robustness of the proposed method. Obtained results show ATDE to be more efficient than other recent advanced methods in the literature. The code of ATDE is publicly available at https://github.com/HuyMax/ATDE-Truss-Frequency.

Optimization of energy-efficient open shop scheduling with an adaptive multi-objective differential evolution algorithm

There have been growing interests in the energy-efficient production scheduling recently because of the growing shortage of energy. Open shop scheduling problem (OSSP) is a kind of common but seldom concerned production scheduling problem. This paper focuses on energy-efficient OSSP (EOSSP), where the effect of setup operation on production efficiency and energy consumption is considered. A multi-objective energy-efficient model based on machine speed scaling mechanism is proposed. To handle this multi-objective problem, we propose an effective adaptive multi-objective differential evolution (AMODE) algorithm. The AMODE uses a new fitness evaluation mechanism (FEM) based on dynamic reference point and fuzzy correlation entropy analysis to assess the solutions in evolution population. It also uses an adaptive opposition-based learning (AOBL) to improve its local search ability. Taguchi method is utilized to obtain the best combination of critical parameters of the AMODE. The proposed mathematical model is validated with CPLEX, and a lexicographic method is used to determine the preferable solution. Experimental results show that both our proposed FEM and AOBL can improve the performance of AMODE. Extensive experiments reveal that the performance of AMODE is superior to the other three well-known algorithms in addressing the EOSSP.

Esign and Application of Optimization Algorithm for Vegetable Planting and Distribution Based on Adaptive Differential Evolution Algorithm

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application.

LED Spectral Matching Based on Adaptive Differential Evolution Algorithm

LED Spectral Matching Based on Adaptive Differential Evolution Algorithm

An Adaptive Differential Evolution Algorithm with a Point-Based Approach for 3D Point Cloud Registration

An Adaptive Differential Evolution Algorithm with a Point-Based Approach for 3D Point Cloud Registration

An adaptive differential evolution algorithm with elite gaussian mutation and bare-bones strategy.

Both differential evolution algorithm (DE) and Bare-bones algorithm (BB) are simple and efficient, but their performance in dealing with complex multimodal problems still has room for improvement. DE algorithm has great advantages in global search and BB algorithm has great advantages in local search. Therefore, how to combine these two algorithms' advantages remains open for further research. An adaptive differential evolution algorithm based on elite Gaussian mutation strategy and bare-bones operations (EGBDE) is proposed in this paper. Some elite individuals are selected and then the mean and the variance of the bare-bones operation are adjusted with the information from the selected elite individuals. This new mutation strategy enhances the global search ability and search accuracy of differential evolution with parameters free. It also helps algorithm get a better search direction and effectively balance the exploration and exploitation. An adaptive adjustment factor is adopted to dynamically balance between differential mutation strategy and the elite Gaussian mutation. Twenty test functions are chosen to verify the performance of EGBDE algorithm. The results show that EGBDE has excellent performance when comparing with other competitors.

Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process.