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

Abstract

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.

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

“…To investigate the performance and effectiveness of the SRIFA, these results are compared with five metaheuristic algorithms. These algorithms are stochastic ranking with a particle-swarm-optimization (SRPSO) [46], self adaptive mix of particle-swarm-optimization (SAMO-PSO) [47], upgraded firefly algorithm (UFA) [37], an ensemble of constraint handling techniques for evolutionary-programming (ECHT-EP2) [48] and a novel differential-evolution algorithm (NDE) [49]. To evaluate proper comparisons of these algorithms, the same number of function evaluations (NFEs = 240,000) were chosen.…”

“…This proposed SRIFA approach is compared to SRPSO [46], MVDE [54], BA [44], MBA [55], JAYA [56], PVS [57], UABC [58], IPSO [59] and AFA [33]. The comparative results obtained by SRIFA for nine NIAs are given in Table 11.…”

Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

“…(2) Calculate the boundaries [ , ] of the current population according to (7) = min ( , ) , = max ( , ) .…”

“…Over the past several years, some population-based random optimization techniques have been widely used to solve optimization problems, such as genetic algorithms (GA) [1], evolutionary programming (EP) [2], particle swarm optimization (PSO) [3], differential evolution (DE) [4], ant colony optimization (ACO) [5], and artificial bee colony (ABC) [6]. Due to PSO's simple concept, easy implementation, yet effective, has been widely applied to various optimization areas [7][8][9][10][11].…”

Constrained optimisation and robust function optimisation with EIWO

“…Hence, researchers have shown increasing interest in utilising stochastic algorithms that can tackle these problems efficiently. Several variants of meta-heuristic algorithms have been proposed during the past few years to cope with the constrained and unconstrained optimisation problems (Afshar, 2007;Ali et al, 2012;Chatterjee et al, 2012;He and Wang, 2007;Kashan, 2011;Ji et al, 2006;Karaboga and Akay, 2011;Noman and Iba, 2011;Rao et al, 2011;Segundo et al, 2012;Srivastava et al, 2012;Tsoulos, 2009;Yang and Deb, 2012;Yuchi and Kim, 2006). ) and the type of variables used in the problem (integer, real, etc.…”