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This study develops a novel variant of particle swarm optimization (PSO), which improves its balance of exploration and exploitation by modifying neighborhood topology, self-adaptive parameter strategies and deep search, namely differential evolutionary evolution PSO with orthogonal learning (OL), i.e., DEEPSO-OL in short. Evolutionary computing can explore the solution space efficiently because of its self-evolving attribute as iteration continues. The OL enhances its exploitation by focusing on deeper search for promising solutions. It utilizes the concept of orthogonal experimental design (OED) which predicts the best combination of control variables without exhaustive evaluation of all possible combinations. In addition, to avoid premature convergence in a local optimum, a stochastic star topology for particles is proposed. Such topology ensures just enough communication among the best performing particles, while encouraging them to explore other spaces. The efficacy of the algorithm is evaluated through real-world scenarios such as optimal power flow (OPF) and wind integrated OPF, which are hard to solve with classical mathematical methods. The proposed algorithm is run on a modified IEEE 30-bus test system and compared to the state-of-the-art evolutionary computing algorithms for a variety of cost objective functions with high levels of non-linearity and non-convexity. The DEEPSO-OL demonstrates its performance to generate more accurate feasible solutions and construct promising and efficient search method for real-world complex optimization problems. • Propose a novel PSO variant method based on orthogonal learning to balance exploration and exploitation. • Apply to a real-world non-linear optimization OPF problem. • Develop a wind energy conversion system model WOPF for wind integrated optimal power flow. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSN), we often detect the monitoring areas among different sensors so that the sensors can be switched on and off adaptively to save energy and extend their lifetime. Inspired by the principle of WSN, a WSN-based adaptive differential evolution (WSNADE) algorithm is proposed in this paper, together with a WSN-based adaptive niching technique (WANT) and two novel strategies called protection-based dual-scale mutation (PDM) strategy and multi-level reset (MLR) strategy, for solving multimodal optimization problems (MMOPs). In WANT, each individual is considered as a sensor with its monitoring area. If the monitoring areas of two individuals intersect, which means these two individuals monitor the similar area and should be partitioned into the same niche. In this way, WANT can adaptively form a niche for each individual, avoiding the sensitivity of niching parameters. Based on WANT, the PDM strategy is designed to select the appropriate mutation strategy for each individual. Besides, to save fitness evaluations (FEs) for exploring more promising areas, the MLR strategy is developed to store the promising individuals and reset the stagnant individuals. The experimental results on 20 multimodal benchmark test functions in CEC2015 multimodal competition show that the proposed WSNADE algorithm generally performs better than or at least comparable with other state-of-the-art multimodal algorithms, including the winner of the CEC2015 competition. Finally, WSNADE is applied to a real-world multimodal application in multiple competitive facilities location design (MCFLD) problem to illustrate its practical applicability. • Design a WSN-based adaptive niching technique (WANT) to form niches adaptively. • Propose a protection-based dual-scale mutation (PDM) strategy. • Design a multi-level reset (MLR) strategy to save and reset individuals. • Illustrate the algorithm advantages in MMOPs and multimodal application. [ABSTRACT FROM AUTHOR]

Localization technology has been a core component for Internet of Things (IoT), especially for Wireless Sensor Network (WSN). Among all localization technologies, Distance Vector-Hop (DV-Hop) algorithm is a very frequently used algorithm for WSN. DV-Hop estimates the distance through the hop-count between nodes in which the value of hop-count is discrete, and thus there is a serious consequence that some nodes have the same estimated distance when their hop-count with respect to identical node is equal. In this paper, we ameliorate the value of hop-count by the number of common one-hop nodes between adjacent nodes. The discrete values of hop-count will be converted to more accurate continuous values by our proposed method. Therefore, the error caused by the estimated distance can be effectively reduced. Furthermore, we formulate the location estimation process to be a minimizing optimization problem based on the weighted squared errors of estimated distance. We apply Differential Evolution (DE) algorithm to acquire the global optimum solution which corresponds to the estimated location of unknown nodes. The proposed localization algorithm based on improved DV-Hop and DE is called DECHDV-Hop. We conduct substantial experiments to evaluate the effectiveness of DECHDV-Hop including the comparison with DV-Hop, GADV-Hop and PSODV-Hop in four different network simulation situations. Experimental results demonstrate that DECHDV-Hop can achieve much higher localization accuracy than other algorithms in these network situations. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient global optimizer, while the covariance matrix adaptation evolution strategy (CMA-ES) shows great power on local search. However, utilizing both of these advantages in one algorithm is difficult since the randomness introduced by DE may reduce the reliability of covariance matrix estimation. Moreover, the exploration ability of DE can be canceled out by CMA-ES because they use completely different mechanisms to control the search step. To take advantage of both DE and CMA-ES, we propose a novel DE variant with covariance matrix self-adaptation, named DECMSA. In DECMSA, a new mutation scheme named “DE/current-to-better/1” is implemented. This scheme uses a Gaussian distribution to guide the search and strengthens both exploration and exploitation capabilities of DE. The proposed algorithm has been tested on the CEC-13 benchmark suite. The experimental results demonstrate that DECMSA outperforms popular DE variants, and it is quite competitive with state-of-the-art CMA-ES variants such as IPOP-CMA-ES and BIPOP-CMA-ES. Moreover, equipped with a constraint handling method, DECMSA is able to produce better solutions than other comparative algorithms on three classic constrained engineering design problems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithm is a population-based algorithm designed for global optimization of the optimization problems. This paper proposes a different DE algorithm based on mathematical modeling of socio-political evolution which is called Colonial Competitive Differential Evolution (CCDE). The two typical CCDE algorithms are benchmarked on three well-known test functions, and the results are verified by a comparative study with two original DE algorithms which include DE/best/1 and DE/rand/2. Also, the effectiveness of CCDE algorithms is tested on Economic Load Dispatch (ELD) problem including 10, 15, 40, and 140-unit test systems. In this study, the constraints and operational limitations, such as valve-point loading, transmission losses, ramp rate limits, and prohibited operating zones are considered. The comparative results show that the CCDE algorithms have good performance and are reliable tools in solving ELD problem. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm designed to find the optimal parameters of PID controller. The proposed algorithm is based on hybridizing between differential evolution (DE) and Particle Swarm Optimization with an aging leader and challengers (ALC-PSO) algorithms. The proposed algorithm (ALC-PSODE) is tested on twelve benchmark functions to confirm its performance. It is found that it can get better solution quality, higher success rate in finding the solution and yields in avoiding unstable convergence. Also, ALC-PSODE is used to tune PID controller in three tanks liquid level system which is a typical nonlinear control system. Compared to different PSO variants, genetic algorithm (GA), differential evolution (DE) and Ziegler–Nichols method; the proposed algorithm achieve the best results with least standard deviation for different swarm size. These results show that ALC-PSODE is more robust and efficient while keeping fast convergence. [ABSTRACT FROM AUTHOR]

Engineering problems presenting themselves in a multiobjective setting have become commonplace in most industries. In such situations the decision maker (DM) requires several solution options prior to selecting the best or the most attractive solution with respect to the current industrial circumstances. The weighted sum scalarization approach was employed in this work in conjunction with three metaheuristic algorithms: particle swarm optimization (PSO), differential evolution (DE) and the improved DE algorithm (GTDE) (which was enhanced using ideas from evolutionary game theory). These methods are then used to generate the approximate Pareto frontier to the nano-CMOS voltage-controlled oscillator (VCO) design problem. Some comparative studies were then carried out to compare the proposed method as compared to the standard DE approach. Examination on the quality of the solutions across the Pareto frontier obtained using these algorithms was carried out using the hypervolume indicator (HVI). [ABSTRACT FROM AUTHOR]

As a promising renewable energy resource, pressure retarded osmosis (PRO) is developing rapidly. Under the fluctuating environmental condition, fewer oscillations and higher convergence speeds are necessary for a stable operation of the PRO system and higher energy extraction. Metaheuristic algorithms are potential techniques for PRO at an accelerating rate, but the balance between the exploitation and exploration process is an inherent challenge in real-time efficiency and accuracy. In this work, a differential evolution (DE) based henry gas solubility optimization (EHO) is proposed for the scaled-up PRO module based on experimental data with respect to varying operational situations. In EHO, the DE mechanism and levy flight technique are applied to enhance the reliability and effectiveness of the classic HGSO strategy. The most advanced intelligent algorithms, including DFOA, GWO and WOA, are conducted for competitive research for verification purposes. Moreover, the superiority of the proposed algorithm has been evaluated and validated in complex operational environments under variations in temperature, draw concentrations and flow rates levels. The modelling results indicate that compared with the classic HGSO method, the proposed method leads to an improvement of the extracted specific energy of the PRO system by an astonishing 84.21%, 111.11% and 175.03%, respectively. • An efficient optimization method, differential evolution-based henry gas solubility optimization (EHO) is proposed. • Dynamic differential evolution and Levy flight mechanism are adopted to enhance reliability and effectiveness. • Results are compared with dragonfly optimization algorithm, whale optimization with differential evolution, grey wolf optimization, particle swarm optimization and Henry gas solubility optimization to verify the convergence and the dynamic performance of EHO. • The problem of maximum power tracking under rapidly varying environmental salinities and temperature conditions in pressure retarded osmosis systems is successfully tackled with EHO. • The convergence performance of the PSO, DFOA and HGSO methods based MPPT of practical PV system are included in the paper, showing the consistency of the speed and performance in maximum power point tracking in the PRO system. [ABSTRACT FROM AUTHOR]

Highlights: [•] An evolutionary algorithm named MOFOA is proposed, which is the first study that extends the relatively new fireworks optimization heuristic for multiobjective optimization. [•] Differential evolution operators are integrated into the algorithm to diversify the search. [•] The algorithm is successfully applied to a number of oil crop variable-rate fertilization (VFR) problems, including a real-world application in east China. [ABSTRACT FROM AUTHOR]

Highlights: [•] Major assumption-entropy is the driving force similar to the convection forces. [•] Outperforms the hybrid ACO2/PSO/BBO classifier. [•] Classifies homogeneous regions more efficiently than others developed till date. [•] Proves to be the best known classifier developed till date. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel differential evolution (DE) algorithm with intersect mutation operation called intersect mutation differential evolution (IMDE) algorithm. Instead of focusing on setting proper parameters, in IMDE algorithm, all individuals are divided into the better part and the worse part according to their fitness. And then, the novel mutation and crossover operations have been developed to generate the new individuals. Finally, a set of famous benchmark functions have been used to test and evaluate the performance of the proposed IMDE. The experimental results show that the proposed algorithm is better than, or at least comparable to the self-adaptive DE (JDE), which is proven to be better than the standard DE algorithm. In further study, the IMDE algorithm has also been compared with several improved Particle Swarm Optimization (PSO) algorithms, Artificial Bee Colony (ABC) algorithm and Bee Swarm Optimization (BSO) algorithm. And the IMDE algorithm outperforms these algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents a new modeling method for nonlinear dynamic systems based on using bilinear series model. Basically, bilinear model is an extension of infinite impulse response (IIR) filter and belongs to the recursive nonlinear system model, i.e., its past output signals will heavily affect the present output. This kind of model can efficiently approximate a large class of nonlinear systems with fewer parameters than other non-recursive models. To adjust the model kernels, we here adopt an evolutionary computation called the differential evolution (DE) algorithm. This algorithm is based on real-valued manipulations and has a good convergence property for finding the global solution or the near global solution of optimized problem. Design steps of DE-based nonlinear system modeling are clearly given in this study. Finally, two kinds of digital systems are illustrated to demonstrate the efficiency of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper describes the results of initial experiments to apply computational algorithms to explore a large parameter space containing many variables in the search for an optimal solution for the sustainable design of an urban development using a potentially complicated fitness function. This initial work concentrates on varying the placement of buildings to optimise solar irradiation availability. For this we propose a hybrid of the covariance matrix adaptation evolution strategy (CMA-ES) and hybrid differential evolution (HDE) algorithms coupled with an efficient backwards ray tracing technique. In this paper we concentrate on the formulation of the new hybrid algorithm and its testing using standard benchmarks as well as a solar optimisation problem. The new algorithm outperforms both the standalone CMA-ES and HDE algorithms in benchmark tests and an alternative multi-objective optimisation tool in the case of the solar optimisation problem. [Copyright &y& Elsevier]

The main aim of this work consists of proposing a new three-step adjusting approach for an improved version of PID-type fuzzy structure in order to determine its design parameters based on a novel hybrid PSO search technique called PSOSCALF, combining Sine Cosine Algorithm (SCA) and Levy Flight (LF) distribution. In addition, conventional and self-tuning controllers are designed to get a better understanding of the performance and robustness of the proposed PID-type FLC approach. At first, the proposed PID-type FLC structure is defined as an optimization problem and then the PSOSCALF algorithm is applied to resolve it systematically. Evaluation of the performance quality of the proposed fuzzy structure is accomplished based on the stabilization and tracking control of a nonlinear Inverted Pendulum (IP) system. To make a complete comparison, the performance of three other optimization techniques namely simple PSO, Differential Evolution (DE) and Cuckoo Search (CS) are examined against the hybrid PSOSCALF algorithm. The simulation results demonstrate that the proposed PSOSCALF-tuned PID-type FLC structure is able to decrease the overshoot and integral square error amounts by about 25% and 10%, respectively compared to the self-tuning controllers. Finally, for more validation, all the controllers are tested under four different disturbance scenarios. Obtained results show that the proposed PID-type FLC can better stabilize the pendulum angle under all the scenarios compared to the PID and self-tuning controllers. • Designing of an improved PID-type FLC and a regular PID controller. • Developing of a new adjusting approach based on hybrid algorithm (PSOSCALF). • Applying the PSOSCALF-based controllers to a nonlinear IP system. • Evaluating of the PID-type FLC performance based on PSOSCALF, PSO, DE and CS. • The PSOSCALF-based PID-FLC is superior to the other controllers. [ABSTRACT FROM AUTHOR]