Layak Ali

Integrated Learning Particle Swarm Optimizer for global optimization

This study proposes a novel Integrated Learning Particle Swarm Optimizer (ILPSO), for optimizing complex multimodal functions. The algorithm modifies the learning strategy of basic PSO to enhance the convergence and quality of solution. The ILPSO approach finds the diverged particles and accelerates them towards optimal solution. This novel study also introduces the particles updating strategy based on hyperspherical coordinates system. This is especially helpful in handling evenly distributed multiple minima. The proposed technique is integrated with comprehensive learning strategy to explore the solution effectively. The performance comparison is carried out against different high quality PSO variants on the set of standard benchmark functions with and without coordinate rotation and with asymmetric initialization. Proposed ILPSO algorithm is efficient in terms of convergence rate, solution accuracy, standard deviation, and computation time compared with other PSO variants. Friedman non-parametric statistical test followed by Dunn post analysis results indicate that the proposed ILPSO algorithm is an effective technique to optimize complex multimodal functions of higher dimension.

Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems

Most of the real world science and engineering optimisation problems are non-linear and constrained. This paper presents a hybrid algorithm by integrating particle swarm optimisation with stochastic ranking for solving standard constrained numerical and engineering benchmark problems. Stochastic ranking technique that uses bubble sort mechanism for ranking the solutions and maintains a balance between the objective and the penalty function. The faster convergence of particle swarm optimisation and the ranking technique are the major motivations for hybridising these two concepts and to propose the stochastic ranking particle swarm optimisation (SRPSO) technique. In this paper, SRPSO is used to optimise 15 continuous constrained single objective benchmark functions and five well-studied engineering design problems. The performance of the proposed algorithm is evaluated based on the statistical parameters such mean, median, best, worst values and standard deviations. The SRPSO algorithm is compared with six recent algorithms for function optimisation. The simulation results indicate that the SRPSO algorithm performs much better while solving all the five standard engineering design problems where as it gives a competitive result for constrained numerical benchmark functions.

The hyperspherical acceleration effect particle swarm optimizer

This paper proposes Hyperspherical Acceleration Effect Particle Swarm Optimization (HAEPSO) for optimizing complex, multi-modal functions. The HAEPSO algorithm finds the particles that are trapped in deep local minima and accelerates them in the direction of global optima. This novel technique improves the efficiency by manipulating PSO parameters in hyperspherical coordinate system. Performance comparisons of HAEPSO are provided against different PSO variants on standard benchmark functions. Results indicate that the proposed algorithm gives robust results with good quality solution and faster convergence. The proposed algorithm is an effective technique for solving complex, higher dimensional multi-modal functions.

Adaptive Accelerated Exploration Particle Swarm Optimizer for global multimodal functions

This paper presents a novel variant of Particle Swarm Optimization (PSO) called Adaptive Accelerated Exploration Particle Swarm Optimizer (AAEPSO). AAEPSO algorithm identifies the particles which are far away from the goal and accelerate them towards goal with an exploration power. These strategies particularly avoid the premature convergence and improve the quality of solution. The performance comparisons of search efficiency, quality of solution and stability of the proposed algorithm are provided against (Differential Evolution) DE, Evolutionary Strategy (ES), Artificial Bee Colony Optimization (ABC) and Particle Swarm Optimization (PSO) algorithms. The comparison is carried out on the set of 10, 30 and 50 dimension complex multimodal benchmark functions. Simulation results indicate the superiority of the proposed AAEPSO over existing algorithms in terms of efficiency, quality solution and stability.

Stochastic Ranking Particle Swarm Optimization for Constrained Engineering Design Problems

This paper presents a novel hybrid algorithm by integrating particle swarm optimization with stochastic ranking for solving standard constrained engineering design problems. The proposed hybrid algorithm uses domain independent characteristics of stochastic ranking and faster convergence of particle swarm optimization. Performance comparison of the proposed algorithm with other popular techniques through comprehensive experimental investigations establishes the effectiveness and robustness of the proposed algorithm for solving engineering design problems.

MESFET small signal model parameter extraction using particle swarm optimization

This paper presents a novel particle swarm based optimization technique to extract small signal equivalent circuit model parameters of a fabricated GaAs MESFET device. The small signal model includes 16 different circuit elements and all are successfully and accurately extracted using the proposed technique. The proposed technique overcomes the difficulties of initial guess of solution and low convergence rate that occurs in conventional optimizer. The effectiveness of results show that the proposed algorithm is robust and accurately provides physically meaningful values for all the circuit elements. The efficiency of this approach is also demonstrated by the results that provides a good fit between measured and modeled S-parameter data over broad range of frequency between 0.5 to 25 GHz.

MESFET DC model parameter extraction using adaptive accelerated exploration particle swarm optimizer

This paper presents an application of Adaptive Accelerated Exploration Particle Swarm Optimization (AAEPSO) algorithm for extracting DC model parameters of a fabricated GaAs based Metal Extended Semiconductor Field Effect Transistor (MESFET). The AAEPSO algorithm is a variant of Particle swarm optimization algorithm that has proven to outperfrom basic PSO in solving benchmark problems. In this work we applied this algorithm to extract the MESFET model parameters by minimizing the error between the measured and modeled drain current. The performance of this approach is compared with popular algorithms like Simulated Annealing, Complex Method (CM), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms based on the (i) mean square error between the measured and modeled drain current, and (ii) convergence time. The comprehensive analysis of AAEPSO is carried out on four different MESFET DC models. Simulation results indicate that the AAEPSO algorithm gives good qaulity of solution in all the cases where as complex method takes less time for executing each iteration.

Particle swarm optimisation with adaptive neighbourhood search for solving multi-objective optimisation problems

This paper proposes a new variant of PSO algorithm with adaptive neighbourhood search of Pareto solutions to solve multi-objective optimisation problems. This algorithm essentially consists of identifying the particles responsible for worst solution and accelerating these particles towards the best solution in an adaptive manner. This concept is hybridised with the updation strategy of non-dominated solution using crowding distance. Performance of the proposed algorithm is demonstrated by solving standard multi-objective benchmark problems including CEC 2009. In particular we compare our method with two recent algorithms by considering the inverted generational distance, spacing, convergence and diversity as the performance metrics. The proposed algorithm is also compared with recent state of art methods in terms of inverted generational distance. The results demonstrate that the proposed algorithm is quite competitive in terms of the mentioned performance indicators as compared to other algorithms.

Adaptive and Accelerated Exploration Particle Swarm Optimizer (AAEPSO) for Solving Constrained Multiobjective Optimization Problems

Many science and engineering design problems are modeled as constrained multiobjective optimization problem. The major challenges in solving these problems are (i) conflicting objectives and (ii) non linear constraints. These conflicts are responsible for diverging the solution from true Pareto-front. This paper presents a variation of particle swarm optimization algorithm integrated with accelerated exploration technique that adapts to iteration for solving constrained multiobjective optimization problems. Performance of the proposed algorithm is evaluated on standard constrained multiobjective benchmark functions (CEC 2009) and compared with recently proposed DECMOSA algorithm. The comprehensive experimental results show the effectiveness of the proposed algorithm in terms of generation distance, diversity and convergence metric.

Accelerated exploration Particle Swarm Optimizer-AEPSO

This paper introduces a novel variant of PSO called accelerated exploration particle swarm optimizer (AEPSO). The AEPSO algorithm select the particles that are far away from the global solution and accelerates them towards global optima with an exploration power to avoid the premature convergence. The performance comparisons such as search efficiency, quality of solution and algorithmic complexity of the proposed algorithm are provided against different high performance PSOs. The comparison is carried out on the set of 30 and 50 dimensional complex multimodal benchmark functions with and without coordinate rotation. Simulation results indicate that the proposed algorithm gives robust results with good quality solution and faster convergence.