Dynamic Population Size Research Articles

Furnace operation optimization with hybrid model based on mechanism and data analytics

The operation optimization problem of furnace process (OOPFP) is to obtain an optimal setting of furnace temperatures to make the reheated slabs have suitable temperature distribution with minimum energy consumption and oxide loss. Since the furnace process is complex, dynamic, and nonlinear, it is difficult to establish a precise process model for the OOPFP. In this paper, firstly, a novel hybrid modeling method combining temperature mechanism with data analytics is proposed to map the relationship of the temperature variables in the furnace process more accurately. The main idea of the hybrid modeling is to compensate for the deviation of original temperature mechanism model through the least squares support vector machine. Then, an improved differential evolution (DE) with feasible region dynamic adjustment strategy and population size gradual shrinking strategy is proposed to solve the OOPFP under hybrid model. Finally, extensive numerical experiments are carried out to demonstrate the effectiveness and accuracy of the hybrid model, and evaluate the performance of the improved DE on solving OOPFP. Experimental results show that the hybrid model is more precise than the original mechanism one, and the proposed DE outperforms other representative algorithms, respectively.

Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA.

Extinction pathways and outbreak vulnerability in a stochastic Ebola model.

A zoonotic disease is a disease that can be passed from animals to humans. Zoonotic viruses may adapt to a human host eventually becoming endemic in humans, but before doing so punctuated outbreaks of the zoonotic virus may be observed. The Ebola virus disease (EVD) is an example of such a disease. The animal population in which the disease agent is able to reproduce in sufficient number to be able to transmit to a susceptible human host is called a reservoir. There is little work devoted to understanding stochastic population dynamics in the presence of a reservoir, specifically the phenomena of disease extinction and reintroduction. Here, we build a stochastic EVD model and explicitly consider the impacts of an animal reservoir on the disease persistence. Our modelling approach enables the analysis of invasion and fade-out dynamics, including the efficacy of possible intervention strategies. We investigate outbreak vulnerability and the probability of local extinction and quantify the effective basic reproduction number. We also consider the effects of dynamic population size. Our results provide an improved understanding of outbreak and extinction dynamics in zoonotic diseases, such as EVD.

Optimal design of corona ring on HV composite insulator using PSO approach with dynamic population size

This paper deals with the use of corona ring at the HV end fitting for improving the electric field and potential distributions and then for minimizing the corona discharges on 230 kV AC transmission line composite insulator. A single corona ring is installed at the energized end side. Three-dimensional finite element method (FEM) software is employed to compute the electric field. As the performance of high voltage insulator strings closely depends on designs and locations of corona ring, the effects of the corona ring radius, the ring tube radius and the ring vertical position are examined. The minimization of the electric field necessitates the optimization of corona ring. For this purpose, new nonlinear mathematical objective function linking the electric field strength to the corona ring structure parameters is established. The optimization problem is achieved by minimizing the objective function using a modified particles swarm optimization (PSO) algorithm with a dynamic population size. The algorithm adjusts the size of population for each iteration. Based on the average value and the best solution of the objective function, we propose a new mathematical model to update the population size. This algorithm enables the population size reduction leading to computing time decrease. According to the results, FEM-PSO hybridization technique could be very helpful in optimization of corona ring design.

Dynamic Population Size and Mutation Round Strategy Assisted Modified Particle Swarm Optimization with Mutation and Reposition

Particle swarm optimization (PSO) is a population-based stochastic search algorithm. This algorithm is utilized to solve the optimization problem. It suffers the problems of trapping in local optimum and premature convergence. Nowadays, both problems can be solved by mutation and reposition techniques apply with PSO (MRPSO). However, this technique use over evaluation calls for solution searching. This research paper proposed a technique to minimized evaluation call. This proposed is called DPM-MRPSO. The concept of adaption evaluation call depends on results from improvement solution of mutation technique and PSO technique. The proposed technique is tested on twenty-four benchmark functions and obtains satisfied search results.

A Novel Artificial Bee Colony Optimizer with Dynamic Population Size for Multi-level Threshold Image Segmentation

Existing swarm intelligence (SI) models are usually derived from fixed-population biological system. However, this approach inevitably causes unnecessary computational cost. In addition, the population size of these models is usually hard to be pr-determined appropriately. In this contribution, this paper exploits a general varying-population swarm model (VPSM) with life-cycle foraging rules based on the population growth dynamic principle. This model essentially improves individual-level adaptability and population-level emergence to self-adapt towards an optimal population size. Then, a novel VPSM-based artificial bee colony optimiser is instantiated with orthogonal Latin squares approach and crossover-based social learning strategies. A comprehensive experimental analysis is implemented in which the proposed algorithm is benchmarked against classical bio-mimetic algorithms on CEC2014 test suites. Then, this algorithm is applied for multi-level image segmentation. Computation results show the performance superiority of the proposed algorithm.

Optimization of hydro energy storage plants by using differential evolution algorithm

The optimal dispatching of cascade Hydro Power Plants is known as a complex optimization problem. In order to solve this problem the authors have applied an adapted differential evolution algorithm by using a fixed and dynamic population size. According to the dynamic population size, the proposed algorithm uses novel random and minimum to maximum sort strategy in order to create new populations with decreased or increased sizes. This implementation enables global search with fast convergence. It also uses a multi-core processor, where all the necessary optimization data are sent to the individual core of a central processing unit. The main aim of the optimization process is to satisfy 24 h demand by minimizing the water quantity used per electrical energy produced. This optimization process also satisfies the desired reservoir levels at the end of the day. The models used in this paper were the real parameters' models of eight cascade Hydro Power Plants located in Slovenia (Europe). Also the standard model from the literature is used in order to compare the performance of the adapted optimization algorithm.

Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem

Incremental Artificial Bee Colony algorithm with Local Search (IABC-LS) is one of efficient variant of artificial bee colony optimization which was successfully applied to economic power dispatch problems before. However IABC-LS algorithm has some tunable parameters which are directly affecting the algorithm behavior. In this study, we have introduced a new algorithm namely Artificial Bee Colony with Dynamic Population size (ABCDP) which is using similar mechanisms defined in IABC-LS without using many parameters to be tuned. To prove the efficiency and robustness of algorithm in power dispatch, the algorithm is used for the combined economic and emission dispatch problem which is converted into single objective optimization problem. For fair comparison, the parameters of both IABC and ABCDP algorithms are determined via automatic parameter configuration tool, Iterated F-Race. IEEE 30 bus test system and 40-generator units problem are used as the problem instances. The results of the algorithms indicate that ABCDP is giving good results in both systems and very competitive with the state-of-the-art.

Multiobjective Optimization of Post Insulator Based on Dynamic Population Size

Most of the existing optimization algorithms use fixed size of population. We propose a dynamic population size throughout the optimization process applied on the numerical model of a medium voltage post insulator. Our modified PSO algorithm enables change population in any iteration of optimization process. It is desired to reduce the population size because of a decreasing calculation time. The results of a modified PSO algorithm are compared with a similar modified differential evolution algorithm. Algorithm PSO is suitably modified in order to operate with the principle of the Pareto nondominancy using dynamic population size.

Fifty years to prove Malthus right

A major question confronting sustainability research today is to what extent our planet, with a finite environmental resource base, can accommodate the faster than exponentially growing human population. Although these concerns are generally attributed to Malthus (1766–1834), early attempts to estimate the maximum sustainable population (ergo, the carrying capacity K) were reported by van Leeuwenhoek (1632–1723) to be at 13 billion people (1). Since then, the concept of carrying capacity has evolved to accommodate many resource limitations originating from available water, energy, and other ecosystem goods and services (1, 2). In PNAS, Suweis et al. (3) apply the concept of carrying capacity using fresh water availability on a national scale as the limiting resource to infer the global K. They estimate a decline in global human population by the middle of this century. We ask to what extent models that are premised on a constant global K as in Suweis et al. (3) agree with an alternative class of carrying capacity models on predicting the timing for unsustainable global population growth. In particular, factors that symbolize innovation and adaptation lead to K representations that are not constant (4, 5) but may depend on a dynamic population size.

Improved Low-Complexity GA-PTS Algorithm Using Dynamic Population Size for OFDM Systems

Improved Low-Complexity GA-PTS Algorithm Using Dynamic Population Size for OFDM Systems

A species-based improved electromagnetism-like mechanism algorithm for TSK-type interval-valued neural fuzzy system optimization

This paper proposes a novel TSK-type interval-valued neural fuzzy system with asymmetric membership functions (TIVNFS-A) which is optimized by a species-based improved electromagnetism-like mechanism (SIEM) algorithm. The proposed TIVNFS-A uses interval-valued asymmetric fuzzy membership functions and the TSK-type consequent part to improve the approximation performance and to reduce the number of fuzzy rules. In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. To train the TIVNFS-As, the SIEM combines the advantages of electromagnetism-like mechanism (EM) algorithm and gradient-descent technique to obtain faster convergence and lower computational complexity. Based on a similarity measurement, the swarm of the SIEM has a dynamic population size, and its population is also divided dynamically into subpopulations for optimization. The gradient-descent method is applied to the dominant particle in each species for a neighborhood search. Finally, the nonlinear system identification and classification problems are shown to demonstrate the performance and effectiveness of the proposed TIVNFS-A with the SIEM.

Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies

Differential Evolution is known for its simplicity and effectiveness as an evolutionary optimizer. In recent years, many researchers have focused on the exploration of Differential Evolution (DE). The objective of this paper is to show the evolutionary and population dynamics for the empirical testing on 3-Parents Differential Evolution (3PDE) for unconstrained function optimization (Teng et al. 2007). In this paper, 50 repeated evolutionary runs for each of 20 well-known benchmarks were carried out to test the proposed algorithms against the original 4-parents DE algorithm. As a result of the observed evolutionary dynamics, 3PDE-SAF performed the best among the preliminary proposed algorithms that included 3PDE-SACr and 3PDE-SACrF. Subsequently, 3PDE-SAF is chosen for the self-adaptive population size for testing dynamic population sizing methods using the absolute (3PDE-SAF-Abs) and relative (3PDE-SAF-Rel) population size encodings. The final result shows that 3PDE-SAF-Rel produced a better performance and convergence overall compared to all the other proposed algorithms, including the original DE. In terms of population dynamics, the population size in 3PDE-SAF-Abs exhibited disadvantageously high dynamics that caused less efficient results. On the other hand, the population size in 3PDE-SAF-Rel was observed to be approximately constant at ten times the number of variables being optimized, hence giving a better and more stable performance.

Distributed differential evolution with explorative–exploitative population families

This paper proposes a novel distributed differential evolution algorithm, namely Distributed Differential Evolution with Explorative---Exploitative Population Families (DDE-EEPF). In DDE-EEPF the sub-populations are grouped into two families. Sub-populations belonging to the first family have constant population size, are arranged according to a ring topology and employ a migration mechanism acting on the individuals with the best performance. This first family of sub-populations has the role of exploring the decision space and constituting an external evolutionary framework. The second family is composed of sub-populations with a dynamic population size: the size is progressively reduced. The sub-populations belonging to the second family are highly exploitative and are supposed to quickly detect solutions with a high performance. The solutions generated by the second family then migrate to the first family. In order to verify its viability and effectiveness, the DDE-EEPF has been run on a set of various test problems and compared to four distributed differential evolution algorithms. Numerical results show that the proposed algorithm is efficient for most of the analyzed problems, and outperforms, on average, all the other algorithms considered in this study.

Parallel Evolutionary Multi-Objective Optimization on Large, Heterogeneous Clusters: An Applications Perspective

Real-world operational use of parallel multi-objective evolutionary algorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heterogeneous computing hardware. This study provides a cross-disciplinary collaborative effort to assess and adapt parallel multi-objective evolutionary algorithms for operational use in satellite constellation design using large dedicated clusters with heterogeneous processor speeds/architectures. A statistical, metric-based evaluation framework is used to demonstrate how time-continuation, asynchronous evolution, dynamic population sizing, and epsilon dominance archiving can be used to enhance both simple master–slave parallelization strategies and more complex multiple-population schemes. Results for a benchmark constellation design coverage problem show that simple master– slave schemes that exploit time-continuation are often sufficient and potentially superior to complex multiple-population schemes.

PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives

Recently, various multiobjective particle swarm optimization (MOPSO) algorithms have been developed to efficiently and effectively solve multiobjective optimization problems. However, the existing MOPSO designs generally adopt a notion to "estimate" a fixed population size sufficiently to explore the search space without incurring excessive computational complexity. To address the issue, this paper proposes the integration of a dynamic population strategy within the multiple-swarm MOPSO. The proposed algorithm is named dynamic population multiple-swarm MOPSO. An additional feature, adaptive local archives, is designed to improve the diversity within each swarm. Performance metrics and benchmark test functions are used to examine the performance of the proposed algorithm compared with that of five selected MOPSOs and two selected multiobjective evolutionary algorithms. In addition, the computational cost of the proposed algorithm is quantified and compared with that of the selected MOPSOs. The proposed algorithm shows competitive results with improved diversity and convergence and demands less computational cost.

How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

Abstract. This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (ε-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses three test cases to compare the algorithms' performances: (1) a standardized test function suite from the computer science literature, (2) a benchmark hydrologic calibration test case for the Leaf River near Collins, Mississippi, and (3) a computationally intensive integrated surface-subsurface model application in the Shale Hills watershed in Pennsylvania. One challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 attained competitive to superior results for most of the problems tested in this study. The primary strengths of the SPEA2 algorithm lie in its search reliability and its diversity preservation operator. The biggest challenge in maximizing the performance of SPEA2 lies in specifying an effective archive size without a priori knowledge of the Pareto set. In practice, this would require significant trial-and-error analysis, which is problematic for more complex, computationally intensive calibration applications. ε-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration. ε-NSGAII's primary strength lies in its ease-of-use due to its dynamic population sizing and archiving which lead to rapid convergence to very high quality solutions with minimal user input. MOSCEM-UA is best suited for hydrologic model calibration applications that have small parameter sets and small model evaluation times. In general, it would be expected that MOSCEM-UA's performance would be met or exceeded by either SPEA2 or ε-NSGAII.

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or “good” initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data.

Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization

Evolutionary algorithms have been recognized to be well suited for multiobjective optimization. These methods, however, need to "guess" for an optimal constant population size in order to discover the usually sophisticated tradeoff surface. This paper addresses the issue by presenting a novel incrementing multiobjective evolutionary algorithm (IMOEA) with dynamic population size that is computed adaptively according to the online discovered tradeoff surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine tuning for broader neighborhood exploration. This achieves better convergence as well as discovering any gaps or missing tradeoff regions at each generation. Other advanced features include a proposed preserved strategy to ensure better stability and diversity of the Pareto front and a convergence representation based on the concept of online population domination to provide useful information. Extensive simulations are performed on two benchmark and one practical engineering design problems.

A multiobjective evolutionary algorithm toolbox for computer-aided multiobjective optimization

This paper presents an interactive graphical user interface (GUI) based multiobjective evolutionary algorithm (MOEA) toolbox for effective computer-aided multiobjective (MO) optimization. Without the need of aggregating multiple criteria into a compromise function, it incorporates the concept of Pareto's optimality to evolve a family of nondominated solutions distributing along the tradeoffs uniformly. The toolbox is also designed with many useful features such as the goal and priority settings to provide better support for decision-making in MO optimization, dynamic population size that is computed adaptively according to the online discovered Pareto-front, soft/hard goal settings for constraint handlings, multiple goals specification for logical "AND"/"OR" operation, adaptive niching scheme for uniform population distribution, and a useful convergence representation for MO optimization. The MOEA toolbox is freely available for download at http://vlab.ee.nus.edu.sg/-kctan/moea.htm which is ready for immediate use with minimal knowledge needed in evolutionary computing. To use the toolbox, the user merely needs to provide a simple "model" file that specifies the objective function corresponding to his/her particular optimization problem. Other aspects like decision variable settings, optimization process monitoring and graphical results analysis can be performed easily through the embedded GUIs in the toolbox. The effectiveness and applications of the toolbox are illustrated via the design optimization problem of a practical ill-conditioned distillation system. Performance of the algorithm in MOEA toolbox is also compared with other well-known evolutionary MO optimization methods upon a benchmark problem.