MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

The goal of optimizing the best acceptable answer is according to the limitations and needs of the problem. For a problem, there are several different answers that are defined to compare them and select an optimal answer; a function is called a target function. The choice of this function depends on the nature of the problem. Sometimes several goals are together optimized; such optimization problems are called multi-objective issues. One way to deal with such problems is to form a new objective function in the form of a linear combination of the main objective functions. In the proposed approach, in order to increase the ability to discover new position in the Imperialist Competitive Algorithm (ICA), its operators are combined with the particle swarm optimization. The colonial competition optimization algorithm has the ability to search global and has a fast convergence rate, and the particle swarm algorithm added to it increases the accuracy of searches. In this approach, the cosine similarity of the neighboring countries is measured by the nearest colonies of an imperialist and closest competitor country. In the proposed method, by balancing the global and local search, a method for improving the performance of the two algorithms is presented. The simulation results of the combined algorithm have been evaluated with some of the benchmark functions. Comparison of the results has been evaluated with respect to metaheuristic algorithms such as Differential Evolution (DE), Ant Lion Optimizer (ALO), ICA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

Fortran programs for the time-dependent Gross–Pitaevskii equation in a fully anisotropic trap

Particle Swarm Optimization or Differential Evolution—A comparison

Hybrid Lagrange interpolation differential evolution algorithm for path synthesis

Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets

RNGAVXLIB: Program library for random number generation, AVX realization

This paper proposes a new hybrid algorithm of combining the conventional Particle Swarm Optimization (PSO) algorithm with Differential Evolution (DE) strategy, named by the authors as Particle Swarm Differential Evolution Optimization (PSDEO) to enhance better balance between local and global search abilities, while solving the economic load dispatch (ELD) problems considering all practical complex constraints and higher order non-smooth cost functions. Considering advantages and disadvantages of both the methods, PSO exploits and DE explores the search space in PSDEO more efficiently to improve the quality of solution and convergence speed. The PSDEO algorithm is tested for benchmark ELD problems, and results show an improved performance of the proposed PSDEO.

Differential evolution with fusion of local and global search strategies

Optimization is used in diverse areas of science, technology, and business. Metaheuristics are one of the common approaches for solving optimization problems. In this paper, we propose a novel and functional metaheuristic Fisherman Search Procedure (FSP), to solve global optimization problems, which explores new solutions using a combination of guided and local search. We evaluate the performance of FSP on a set of benchmark functions commonly used for testing global optimization methods. We compare FSP with other heuristic methods referenced in the literature, namely differential evolution (DE), particle swarm optimization (PSO), and greedy randomized adaptive search procedures. Results are analyzed in terms of successful runs, i.e., convergence on global minimum values, and time consumption, demonstrating that FSP can achieve very good performances in most of the cases. In 90 % of the cases, FSP is located among the two better results as for successful runs. On the other hand, with regard to time consumption, FSP shows similar results to PSO and DE, achieving the best and second best results for 82 % of the test functions. Finally, FSP showed to be a simple and robust metaheuristic that achieves good solutions for all evaluated theoretical problems.

Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.

Application of fast analytical approach and AI optimization techniques to hydraulic fracture stage placement in shale gas reservoirs

Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.

Plasmonics simulations with the MNPBEM toolbox: Consideration of substrates and layer structures

Delamination is a type of representative damage in composite structures, severely degrading structural integrity and reliability. The identification of delamination is commonly treated as an issue of nondestructive testing. Differing from existing studies, a hybrid optimization algorithm (HOA), combining particle swarm optimization (PSO) with simplex method (SM), is proposed to identify delamination in laminated beams. The objective function of the optimization problem is created using delamination variables (optimization parameters) together with actually measured modal frequencies. The HOA adopts a hierarchical and cooperative regime of global search and local search to optimize the objective function. The PSO performs global search for objective function space to achieve a preliminary solution specifying a local potential space. Initialized by this preliminary solution, the SM executes local search for the local potential space to explore the optimal solution. The HOA is validated by a series of simulated delamination scenarios, and the results show that it can identify delamination in laminated beams with decent accuracy, reliability and efficiency. The method proposed holds promise for establishing online damage detection system beneficial for health monitoring of laminated composite structures.