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A Hybrid Global Numerical Optimization with Combination of Extremal Optimization and Sequential Quadratic Programming

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A Hybrid Global Numerical Optimization with Combination of Extremal Optimization and Sequential Quadratic Programming Journal of Theoretical and Applied Information Technology 20th April 2013. Vol. 50 No.2 © 2005 - 2013 JATIT & LLS. All rights reserved. ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 A HYBRID GLOBAL NUMERICAL OPTIMIZATION WITH COMBINATION OF EXTREMAL OPTIMIZATION AND SEQUENTIAL QUADRATIC PROGRAMMING 1,2PENGCHEN, 3ZAI-SHENG PAN, 4YONG-ZAI LU 1Zhejiang Supcon Research Co., LTD., Hangzhou, P. R. China 2Department of Control science and engineering, Zhejiang University, Hangzhou, P. R. China 3Department of Control science and engineering, Zhejiang University, Hangzhou, P. R. China 4Department of Control science and engineering, Zhejiang University, Hangzhou, P. R. China E-mail: [email protected], [email protected], [email protected] ABSTRACT In recent years, many efforts have focused on cooperative (or hybrid) optimization approaches for their robustness and efficiency to solve decision and optimization problems. This paper proposes a novel hybrid solution with the integration of bio-inspired computational intelligence extremal optimization (EO) and deterministic sequential quadratic programming (SQP) for numerical optimization, which combines the unique features of self-organized criticality (SOC), non-equilibrium dynamics and global search capability in EO with local search efficiency of SQP. The performance of proposed EO-SQP algorithm is tested on twelve benchmark numerical optimization problems and compared with some other state-of-the-art approaches. The experimental results show the EO-SQP method is capable of finding the optimal or near optimal solutions for nonlinear programming problems effectively and efficiently. Keywords: Extremal optimization (EO), Sequential quadratic programming (SQP), Memetic algorithms (MA), Nonlinear programming (NLP), Numerical optimization complex surfaces and inherently better suited for 1. INTRODUCTION avoiding local minima. However, computational intelligence has its weakness in slow convergence With the high demand in decision and and providing a precise enough solution because of optimization for many real-world problems and the the failure to exploit local information [6]. progress in computer science, the research on novel Moreover, for constrained optimization problems global optimization solutions has been a challenge involving a number of constraints with which the to academic and industrial societies. During past optimal solution must satisfy, computational few decades, various optimization techniques have intelligence methods often lack an explicit been intensively studied; those techniques follow mechanism to bias the search in feasible regions different approaches and can be divided roughly [4][7][8]. into three main categories, namely, the deterministic methods [1], stochastic methods [2] During the last decades, a particular class of and bio-inspired computational intelligence [3]. global-local search hybrids named “memetic algorithms” (MAs) are proposed [9], which are In general, most global optimization problems motivated by Richard Dawkins’s theory [10]. MAs are intractable, especially when the optimization are a class of stochastic heuristics for global problem has complex landscape and the feasible optimization which combine the global search region is concave and covers a very small part of nature of EA with local search to improve the whole search space [4]. Solution accuracy and individual solution [11]. They have been global convergence are two important factors in the successfully applied to hundreds of real-world development of optimization techniques. problems such as optimization of combinatorial Deterministic search methods are known to be very optimization [12], multi-objective optimization [13], efficient with high accuracy. Unfortunately, they bioinformatics [14], etc. are easily trapped in local minima [5]. On the other hand, the methods of computational intelligence [3] This paper proposed a novel hybrid EO-SQP are much more effective for traversing these method with the combination of recently proposed 401 Journal of Theoretical and Applied Information Technology 20th April 2013. Vol. 50 No.2 © 2005 - 2013 JATIT & LLS. All rights reserved. ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 extremal optimization (EO) and the popular satisfy p-inequality constraints gXt (), q-equality deterministic sequential quadratic programming constraints hXu (), xv and xv are the lower and (SQP) under the conceptual umbrella of MA. EO is upper bounds of the variable xv . a general-purpose heuristic algorithm, with the superior features of self-organized criticality (SOC), The above formulation is an instance of the well- non-equilibrium dynamics, co-evolutions in known nonlinear programming (NLP) problem. In statistical mechanics and ecosystems respectively general, the global optimization of NLP is one of [15] [16]. SQP has been one of the most popular the toughest NP-hard problems. Solving this type of methods for nonlinear optimization because of its problems has become a challenge to computer efficiency of solving medium and small size science and operations research. nonlinear programming problems [1][17]. It guarantees local optima as it follows a gradient 3. EO-SQP OPTIMIZATION INSPIRED BY search direction from the starting point towards the MEMETIC ALGORITHM optimum point and has special advantages in 3.1 Extremal Optimization dealing with various constraints [18]. This will be particularly helpful for the hybrid EO-SQP The Extremal Optimization (EO) proposed by algorithm when solving constrained optimization Boettcher and Percus [15][16] is derived from the problems: the SQP can also serve as a means of fundamentals of statistical physics and self- “repairing” infeasible solutions during EO organized criticality (SOC) [21] based on Bak- evolution. The proposed method balances both Sneppen (BS) model [22] which simulates far-from aspects through the hybridization of heuristic EO as equilibrium dynamics in statistical physics and co- the global search scheme and deterministic SQP as evolution[23]. Generally speaking, EO is the local search scheme. particularly applicable in dealing with large complex problems with rough landscape, phase The rest of this paper is organized as follows: In transitions passing “easy-hard-easy” boundaries or section 2, the nonlinear optimization problem multiple local optima. It is less likely to be trapped with/without constraints is described in a general in local minima than traditional gradient-based formulation. Section 3 presents the EO-SQP search algorithms. The research results by Chen and fundamental and algorithm in detail. In section 4, Lu show EO and its derivatives can be effectively the proposed approach is used to solve twelve applied in solving multi-objective combinatorial benchmark test functions, and the results are hard benchmarks and real-world optimization quantitatively compared with genetic algorithm problems [24] [25] [26] [27]. (GA), particle swarm optimization (PSO), SQP and some other popular methods such as Stochastic 3.2 Sequential quadratic programming (SQP) Ranking (SR) [8], Simple Multimembered After its initial proposal by Wilson in 1963 [28], Evolution Strategy (SMES) [19] and Auxiliary the sequential quadratic programming (SQP) Function Method (AFM) [20]. Finally, the method was popularized in the 1970’s by Han [29] concluding remarks are addressed in Section 5. and Powell [30]. SQP proves itself as the most successful method and outperforms other nonlinear 2. PROBLEM FORMULATION programming methods in terms of efficiency and Many real-world optimization problems can be accuracy to solve nonlinear optimization problems. mathematically modeled in terms of a desired The solution procedure is on the basis of objective function subject to a set of constraints as formulating and solving a quadratic sub-problem follows: with iterative search. Minimize fX( ), X= [ xx12 , ,..., xn ] (1) 3.3 MA based Hybrid EO-SQP algorithm subject to As mentioned above, conventional optimization techniques based on deterministic rules often fail or ≤= gXt ( ) 0; t1,2,..., p (2) get trapped in local optimum when solving complex problems. In contrast to deterministic optimization hXu ( )= 0; u = 1,2,..., q (3) techniques, many computational intelligence based xxxvvv≤≤; v =1,2,..., n (4) optimization methods are good at global search, but relatively poor in fine-tuned local search when the n where XR∈ is an n-dimensional vector solutions approach to a local region near the global representing the solution of the problem (1) - (4), optimum. According to so-called “No-Free-Lunch” fX() is the objective function, which needs to 402 Journal of Theoretical and Applied Information Technology 20th April 2013. Vol. 50 No.2 © 2005 - 2013 JATIT & LLS. All rights reserved. ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 Theorem by Wolpert and Macready [31], a search Fitnessglobal () S= f () S + Penalty () S (6) algorithm strictly performs in accordance with the ∏∏ghtu, amount and quality of the problem knowledge they Unlike GA, which works with a population of incorporate. This fact clearly underpins the candidate solutions, EO depends on a single exploitation of problem knowledge intrinsic to MAs individual (i.e. chromosome) based evolution. [32]. Under the framework of MAs, the stochastic Through always performing mutation on the worst global search heuristics work together with component and its neighbors successively, the problem-specific solvers, in which Neo- individual in EO can evolve itself toward the global Darwinian’s natural
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