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In this paper, we investigate the ability of two new nature-inspired metaheuristics namely the flower pollination (FP) and the social spiders optimization (SSO) algorithms to solve the image segmentation problem via multilevel thresholding. The FP algorithm is inspired from the biological process of flower pollination. It relies on two basic mechanisms to generate new solutions. The first one is the global pollination modeled in terms of a Levy distribution while the second one is the local pollination that is based on random selection of local solutions. For its part, the SSO algorithm mimics different natural cooperative behaviors of a spider colony. It considers male and female search agents subject to different evolutionary operators. In the two proposed algorithms, candidate solutions are firstly generated using the image histogram. Then, they are evolved according to the dynamics of their corresponding operators. During the optimization process, solutions are evaluated using the between-class variance or Kapur's method. The performance of each of the two proposed approaches has been assessed using a variety of benchmark images and compared against two other nature inspired algorithms from the literature namely PSO and BAT algorithms. Results have been analyzed both qualitatively and quantitatively based on the fitness values of obtained best solutions and two popular performance measures namely PSNR and SSIM indices as well. Experimental results have shown that both SSO and FP algorithms outperform PSO and BAT algorithms while exhibiting equal performance for small numbers of thresholds. For large numbers of thresholds, it was observed that the performance of FP algorithm decreases as it is often trapped in local minima. In contrary, the SSO algorithm provides a good balance between exploration and exploitation and has shown to be the most efficient and the most stable for all images even with the increase of the threshold number. These promising results suggest that the SSO algorithm can be effectively considered as an attractive alternative for the multilevel image thresholding problem. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the . The optimal solutions can be found by the proposed EPCSO method in a very short time. [Copyright &y& Elsevier]

Abstract: The hybrid algorithm that combined particle swarm optimization with simulated annealing behavior (SA-PSO) is proposed in this paper. The SA-PSO algorithm takes both of the advantages of good solution quality in simulated annealing and fast searching ability in particle swarm optimization. As stochastic optimization algorithms are sensitive to their parameters, proper procedure for parameters selection is introduced in this paper to improve solution quality. To verify the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimization functions with different dimensions. The comparative works have also been conducted among different algorithms under the criteria of quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the results, the SA-PSO could have higher efficiency, better quality and faster convergence speed than compared algorithms. [Copyright &y& Elsevier]

Abstract: The job-shop scheduling problem (JSSP) is a branch of production scheduling, and it is well known that this problem is NP-hard. Many different approaches have been applied to JSSP and a rich harvest has been obtained. However, some JSSP, even with moderate size, cannot be solved to guarantee optimality. The standard particle optimization algorithm generally is used to solve continuous optimization problems, and is used rarely to solve discrete problems such as JSSP. This paper presents a similar PSO algorithm to solve JSSP. At the same time, some new valid algorithm operators are proposed in this paper, and through simulation we find out the effectiveness of them. Three representative (Taillard) instances were made by computational experiments, through comparing the SPSO algorithm with standard GA, and we obtained that the SPSOA is more clearly efficacious than standard GA for JSSP to minimize makespan. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the PSO, and then gives a PSO variant without randomness to show that traditional PSO cannot work without randomness. Based on our analysis of the randomness, another way of using randomness is proposed in PSO with random dimension selection (PSORDS) algorithm, which utilizes random dimension selection instead of stochastic coefficients. Finally, deterministic methods to do the dimension selection are proposed, and the resultant PSO with distance based dimension selection (PSODDS) algorithm is greatly superior to the traditional PSO and PSO with heuristic dimension selection (PSOHDS) algorithm is comparable to traditional PSO algorithm. In addition, using our dimension selection method to a newly proposed modified particle swarm optimization (MPSO) algorithm also gets improved results. The experiment results demonstrate that our analysis about the randomness is correct and the usage of deterministic dimension selection method is very helpful. [Copyright &y& Elsevier]

Abstract: The Knapsack Problems (KPs) are classical NP-hard problems in Operations Research having a number of engineering applications. Several traditional as well as population based search algorithms are available in literature for the solution of these problems. In this paper, a new Modified Binary Particle Swarm Optimization (MBPSO) algorithm is proposed for solving KPs, particularly 0–1 Knapsack Problem (KP) and Multidimensional Knapsack Problem (MKP). Compared to the basic Binary Particle Swarm Optimization (BPSO), this improved algorithm introduces a new probability function which maintains the diversity in the swarm and makes it more explorative, effective and efficient in solving KPs. MBPSO is tested through computational experiments over benchmark problems and the results are compared with those of BPSO and a relatively recent modified version of BPSO namely Genotype–Phenotype Modified Binary Particle Swarm Optimization (GPMBPSO). To validate our idea and demonstrate the efficiency of the proposed algorithm for KPs, experiments are carried out with various data instances of KP and MKP and the results are compared with those of BPSO and GPMBPSO. [Copyright &y& Elsevier]

Abstract: This paper presents a novel particle swarm optimization (PSO) algorithm based on Markov chains and competitive penalized method. Such an algorithm is developed to solve global optimization problems with applications in identifying unknown parameters of a class of genetic regulatory networks (GRNs). By using an evolutionary factor, a new switching PSO (SPSO) algorithm is first proposed and analyzed, where the velocity updating equation jumps from one mode to another according to a Markov chain, and acceleration coefficients are dependent on mode switching. Furthermore, a leader competitive penalized multi-learning approach (LCPMLA) is introduced to improve the global search ability and refine the convergent solutions. The LCPMLA can automatically choose search strategy using a learning and penalizing mechanism. The presented SPSO algorithm is compared with some well-known PSO algorithms in the experiments. It is shown that the SPSO algorithm has faster local convergence speed, higher accuracy and algorithm reliability, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. Finally, we utilize the presented SPSO algorithm to identify not only the unknown parameters but also the coupling topology and time-delay of a class of GRNs. [ABSTRACT FROM AUTHOR]

Abstract: Back analysis is commonly used in identifying geomechanical parameters based on the monitored displacements. Conventional back analysis method is not capable of recognizing non-linear relationship involving displacements and mechanical parameters effectively. The new intelligent displacement back analysis method proposed in this paper is the combination of support vector machine, particle swarm optimization, and numerical analysis techniques. The non-linear relationship is efficiently represented by support vector machine. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, a global optimum search on the obtained SVMs by particle swarm optimization can lead to the geomechanical parameters identification effectively. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization is a novel swarm-intelligence-based algorithm and a valid optimization technique. However, the algorithm suffers from the premature convergence problem when facing to complex optimization problem. In order to keep the balance between the global exploration and the local exploitation validly, the paper develops a knowledge-based cooperative particle swarm optimization (KCPSO). KCPSO mainly simulates the self-cognitive and self-learning process of evolutionary agents in special environment, and introduces a knowledge billboard to record varieties of search information. Moreover, KCPSO takes advantage of multi-swarm to maintain the swarm diversity and tries to guide their evolution by the shared information. Under the guide of the shared information, KCPSO manipulates each sub-swarm to go on with local exploitation in different local area, in which every particle follows a social learning behavior mode; at the same time, KCPSO carries out the global exploration through the escaping behavior and the cooperative behavior of the particles in different sub-swarms. KCPSO can maintain appropriate swarm diversity and alleviate the premature convergence validly. The proposed model was applied to some well-known benchmarks. The relative experimental results show KCPSO is a robust global optimization method for the complex multimodal functions. [Copyright &y& Elsevier]

Abstract: This paper presents a novel particle swarm optimization algorithm from the angle of ecological population evolution, called the ecological particle swarm optimization, or EPSO. Initially, ecological population competition model (EPCM) is presented. From the basis of the EPCM, the EPSO algorithm and its general framework are proposed; in which particle swarm system with ecological hierarchy and competition model is defined and two collocating strategies of inertia weight factor are considered. The convergence performance and population dynamics including population aggregation and population diversity of the proposed approach are discussed separately through empirical simulations with well-known benchmarks from the standard literature. [Copyright &y& Elsevier]

Abstract: In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy. These changes and the numerous advantages of the distributed generation units (DGs) in term of their technology enhancement and economical considerations have created more incentives to use these kinds of generators than before. Therefore, it is necessary to study the impact of DGs on the power systems, especially on the distribution networks. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering DGs. The main objective of the DFR is to minimize the deviation of the bus voltage, the number of switching operations and the total cost of the active power generated by DGs and distribution companies. Since the DFR is a nonlinear optimization problem, we apply the particle swarm optimization (PSO) approach to solve it. The feasibility of the proposed approach is demonstrated and compared with other evolutionary methods such as genetic algorithm (GA), Tabu search (TS) and differential evolution (DE) over a realistic distribution test system. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) as an efficient and powerful problem-solving strategy has been widely used, but appropriate adjustment of its parameters usually requires a lot of time and labor. So a co-evolving framework is proposed to improve the robustness of the PSO. In this paper, within this framework the fuzzy rules for the manipulation of the inertia weights are co-evolved with the particles. And the simulation results on a suite of test functions show that the use of this co-evolving framework improves the performance of the PSO, especially the robustness against the dimensional variation of the test functions. [Copyright &y& Elsevier]

Abstract: Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications. [Copyright &y& Elsevier]