Local search with quadratic approximations into memetic algorithms for optimization with multiple criteria

This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cost of function evaluation. The new operator employs quadratic approximations of the objective functions and constraints, which are built using only the function samples already produced by the usual evolutionary algorithm function evaluations. The local search phase consists of solving the auxiliary multiobjective quadratic optimization problem defined from the quadratic approximations, scalarized via a goal attainment formulation using an LMI solver. As the determination of the new approximated solutions is performed without the need of any additional function evaluation, the proposed methodology is suitable for costly black-box optimization problems.     

On user-centric memetic algorithms

Memetic algorithms (MAs) constitute a metaheuristic optimization paradigm [usually based on the synergistic combination of an evolutionary algorithm (EA) and trajectory-based optimization techniques] that systematically exploits the knowledge about the problem being solved and that has shown its efficacy to solve many combinatorial optimization problems. However, when the search depends heavily on human-expert’s intuition, the task of managing the problem knowledge might be really difficult or even indefinable/impossible; the so-called interactive evolutionary computation (IEC) helps to mitigate this problem by enabling the human user to interact with an EA during the optimization process. Interactive MAs can be constructed as reactive models in which the MA continuously demands the intervention of the human user; this approach has the drawback that provokes fatigue to the user. This paper considers user-centric MAs, a more global perspective of interactive MAs since it hints possibilities for the system to be proactive rather than merely interactive, i.e., to anticipate some of the user behavior and/or exhibit some degree of creativity, and provides some guidelines for the design of two different models for user-centric MAs, namely reactive and proactive search-based schema. An experimental study over two complex NP-hard problems, namely the Traveling Salesman problem and a Gene Ordering Problem, shows that user-centric MAs are in general effective optimization methods although the proactive approach provides additional advantages.     

A sociologically inspired heuristic for optimization algorithms: A case study on ant systems

This paper discusses how social network theory can provide optimization algorithms with social heuristics. The foundations of this approach were used in the SAnt-Q (Social Ant-Q) algorithm, which combines theory from different fields to build social structures for state-space search, in terms of the ways that interactions between states occur and reinforcements are generated. Social measures are therefore used as a heuristic to guide exploration and approximation processes. Trial and error optimization techniques are based on reinforcements and are often used to improve behavior and coordination between individuals in a multi-agent system, although without guarantees of convergence in the short term. Experiments show that identifying different social behavior within the social structure that incorporates patterns of occurrence between states explored helps to improve ant coordination and optimization process within Ant-Q and SAnt-Q, giving better results that are statistically significant.     

基于改进的粒子滤波非线性约束优化算法

传统的非线性约束优化算法的精度较低,为了克服这一问题,提出了一种基于粒子滤波的新型优化算法。该算法用于解决非线性约束优化问题,并结合粒子滤波器的模型和机制。首先,利用粒子滤波算法的基本原理建立这种优化算法,并给出算法的操作步骤;然后将非线性约束优化问题转换为函数优化问题函数优化问题,并针对非线性约束优化问题,建立粒子滤波优化算法的数学模型。仿真实验结果证明了这种新型算法的正确性,并且表明了相对于传统的优化算法,基于粒子滤波器的优化方法在解决非线性优化问题方面具有更高的效率和速率,并对今后的非线性约束优化问题具有适应性。     

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.     

Genetic algorithms optimization for normalized normal constraint method under Pareto construction

This paper presents the resolution of multiobjective optimization problems as a tool in engineering design. In the literature, the solutions of this problems are based on the Pareto frontier construction. Therefore, substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The normalized normal constraint is a recent contribution that generates a well-distributed Pareto frontier. Nevertheless, these methods are susceptible of improvement or modifications to obtain the same level of results more efficiently. This paper proposes a modification of the original normalized normal constraint method using a genetic algorithms in the optimization task. The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss–Newton optimization methods which are used by the original normalized normal constraint method.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

Meta-Lamarckian learning in memetic algorithms

Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the limited progress made on mitigating the effects of incorrect local search method choice, we present strategies for MA control that decide, at runtime, which local method is chosen to locally improve the next chromosome. The use of multiple local methods during a MA search in the spirit of Lamarckian learning is here termed Meta-Lamarckian learning. Two adaptive strategies for Meta-Lamarckian learning are proposed in this paper. Experimental studies with Meta-Lamarckian learning strategies on continuous parametric benchmark problems are also presented. Further, the best strategy proposed is applied to a real-world aerodynamic wing design problem and encouraging results are obtained. It is shown that the proposed approaches aid designers working on complex engineering problems by reducing the probability of employing inappropriate local search methods in a MA, while at the same time, yielding robust and improved design search performance.     

Classification of adaptive memetic algorithms: a comparative study.

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.     

A memetic algorithm for multiple-drug cancer chemotherapy schedule optimization.

This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of constraints. We combine the adaptive elitist genetic algorithm with a local search algorithm called iterative dynamic programming (IDP) to form a new memetic algorithm (MA-IDP) for solving the problem. MA-IDP has been shown to be very efficient in solving the multidrug scheduling optimization problem.     

Real-coded memetic algorithms with crossover hill-climbing

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.     

Two memetic algorithms for heterogeneous fleet vehicle routing problems

The vehicle routing problem (VRP) plays an important role in the distribution step of supply chains. From a depot with identical vehicles of limited capacity, it consists in determining a set of vehicle trips of minimum total length, to satisfy the demands of a set of customers. In general, the number of vehicles used is a decision variable. The heterogeneous fleet VRP (HFVRP or HVRP) is a natural generalization with several vehicle types, each type being defined by a capacity, a fixed cost, a cost per distance unit and a number of vehicles available. The vehicle fleet mix problem (VFMP) is a variant with an unlimited number of vehicles per type. This paper presents two memetic algorithms (genetic algorithms hybridized with a local search) able to solve both the VFMP and the HVRP. They are based on chromosomes encoded as giant tours, without trip delimiters, and on an optimal evaluation procedure which splits these tours into feasible trips and assigns vehicles to them. The second algorithm uses a distance measure in solution space to diversify the search. Numerical tests on standard VFMP and HFVRP instances show that the two methods, especially the one with distance measure, compete with published metaheuristics and improve several best-known solutions.     

A Study on the use of ``self-generation' in memetic algorithms

A vast number of very successful applications of Global-Local Search Hybrids have been reported in the literature in the last years for a wide range of problem domains. The majority of these papers report the combination of highly specialized pre-existing local searchers and usually purpose-specific global operators (e.g. genetic operators in an Evolutionary Algorithm).In this paper we concentrate on one particular class of Global-Local Search Hybrids, Memetic Algorithms (MAs), and we describe the implementation of ``self-generating' mechanisms to produce the local searches the MA uses. This implementation is tested in two problems, NK-Landscape Problems and the Maximum Contact Map Overlap Problem (MAX-CMO).     

Multiobjective optimization and multiple constraint handling withevolutionary algorithms. I. A unified formulation

In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and differentiability of the cost surface add yet another conflicting element to the decision process. While “better” solutions should be rated higher than “worse” ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or fitness, to such candidate solutions. After reviewing current revolutionary approaches to multiobjective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape     

A comparative study of the multi-objective optimization algorithms for coal-fired boilers

Combustion optimization has been proved to be an effective way to reduce the NOx emissions and unburned carbon in fly ash by carefully setting the operational parameters of boilers. However, there is a trade-off relationship between NOx emissions and the boiler economy, which could be expressed by Pareto solutions. The aim of this work is to achieve multi-objective optimization of the coal-fired boiler to obtain well distributed Pareto solutions. In this study, support vector regression (SVR) was employed to build NOx emissions and carbon burnout models. Thereafter, the improved Strength Pareto Evolutionary Algorithm (SPEA2), the new Multi-Objective Particle Swarm Optimizer (OMOPSO), the Archive-Based hYbrid Scatter Search method (AbYSS), and the cellular genetic algorithm for multi-objective optimization (MOCell) were used for this purpose. The results show that the hybrid algorithms by combining SVR can obtain well distributed Pareto solutions for multi-objective optimization of the boiler. Comparison of various algorithms shows MOCell overwhelms the others in terms of the quality of solutions and convergence rate.     

A comparative study of multi-objective optimization algorithms for sparse signal reconstruction

The development of the efficient sparse signal recovery algorithm is one of the important problems of the compressive sensing theory. There exist many types of sparse signal recovery methods in compressive sensing theory. These algorithms are classified into several categories like convex optimization, non-convex optimization, and greedy methods. Lately, intelligent optimization techniques like multi-objective approaches have been used in compressed sensing. Firstly, in this paper, the basic principles of the compressive sensing theory are summarized. And then, brief information about multi-objective algorithms, local search methods, and knee point selection methods are given. Afterward, multi-objective sparse recovery methods in the literature are reviewed and investigated in accordance with their multi-objective optimization algorithm, the local search method, and the knee point selection method. Also in this study, examples of multi-objective sparse reconstruction methods are designed according to the existing studies. Finally, the designed algorithms are tested and compared by using various types of sparse reconstruction test problems.

     

Parallel machine scheduling problems using memetic algorithms

In this paper, we investigate how to apply the hybrid genetic algorithms (the memetic algorithms) to solve the parallel machine scheduling problem. There are two essential issues to be dealt with for all kinds of parallel machine scheduling problems: job partition among machines and job sequence within each machine. The basic idea of the proposed method is that (a) use the genetic algorithms to evolve the job partition and then (b) apply a local optimizer to adjust the job permutation to push each chromosome climb to his local optima. Preliminary computational experiments demonstrate that the hybrid genetic algorithm outperforms the genetic algorithms and the conventional heuristics.     

A novel memetic algorithm based on invasive weed optimization and differential evolution for constrained optimization

This paper presents a novel memetic algorithm, named as IWO_DE, to tackle constrained numerical and engineering optimization problems. In the proposed method, invasive weed optimization (IWO), which possesses the characteristics of adaptation required in memetic algorithm, is firstly considered as a local refinement procedure to adaptively exploit local regions around solutions with high fitness. On the other hand, differential evolution (DE) is introduced as the global search model to explore more promising global area. To accommodate the hybrid method with the task of constrained optimization, an adaptive weighted sum fitness assignment and polynomial distribution are adopted for the reproduction and the local dispersal process of IWO, respectively. The efficiency and effectiveness of the proposed approach are tested on 13 well-known benchmark test functions. Besides, our proposed IWO_DE is applied to four well-known engineering optimization problems. Experimental results suggest that IWO_DE can successfully achieve optimal results and is very competitive compared with other state-of-art algorithms.     

Multiobjective memetic algorithms for time and space assembly line balancing

This paper presents three proposals of multiobjective memetic algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. These three proposals are, respectively, based on evolutionary computation, ant colony optimisation, and greedy randomised search procedure. Different variants of these memetic algorithms have been developed and compared in order to determine the most suitable intensification–diversification trade-off for the memetic search process. Once a preliminary study on nine well-known problem instances is accomplished with a very good performance, the proposed memetic algorithms are applied considering real-world data from a Nissan plant in Barcelona (Spain). Outstanding approximations to the pseudo-optimal non-dominated solution set were achieved for this industrial case study.     

Energy-aware topology control for wireless sensor networks using memetic algorithms

Cost-effective topology control is critical in wireless sensor networks. While much research has been carried out in this aspect using various methods, no attention has been made on utilizing modern heuristics for this purpose. This paper proposes a memetic algorithm-based solution for energy-aware topology control for wireless sensor networks. This algorithm (called ToCMA), using a combination of problem-specific light-weighted local search and genetic algorithms, is able to solve the minimum energy network connectivity (MENC) this NP-hard problem in an approximated manner that performs better than the classical minimum spanning tree (MST) solution. The outcomes of ToCMA can also be utilized for various network optimization and fault-tolerant purposes.