Particle Swarm Optimization (PSO) is one of the most widely used heuristic algorithms. The simplicity and inexpensive computational cost makes this algorithm very popular and powerful in solving a wide range of problems. The binary version of this algorithm has been introduced for solving binary problems. The main part of the binary version is a transfer function which is responsible to map a continuous search space to a discrete search space. Currently there appears to be insufficient focus on the transfer function in the literature despite its apparent importance. In this study six new transfer functions divided into two families, s-shaped and v-shaped, are introduced and evaluated. Twenty-five benchmark optimization functions provided by CEC 2005 special session are employed to evaluate these transfer functions and select the best one in terms of avoiding local minima and convergence speed. In order to validate the performance of the best transfer function, a comparative study with six recent modifications of BPSO is provided as well. The results prove that the new introduced v-shaped family of transfer functions significantly improves the performance of the original binary PSO.

S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization

Topology optimization is a highly developed tool for structural design and is by now being extensively used in mechanical, automotive and aerospace industries throughout the world. Gradient-based topology optimization algorithms may efficiently solve fine-resolution problems with thousands and up to millions of design variables using a few hundred (finite element) function evaluations (and even less than 50 in some commercial codes). Nevertheless, non-gradient topology optimization approaches that require orders of magnitude more function evaluations for extremely low resolution examples keep appearing in the literature. This forum article discusses the practical and scientific relevance of publishing papers that use immense computational resources for solving simple problems for which there already exist efficient solution techniques.

On the usefulness of non-gradient approaches in topology optimization

Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specifically-designed diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. This paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, this paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multiobjective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, this paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.

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Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

Democratic PSO for truss layout and size optimization with frequency constraints

The tribal and generic classification of the diverse ant subfamily Ponerinae (Hymenoptera: Formicidae) is revised to reflect recent molecular phylogenetic information and a reappraisal of ponerine morphological diversity. The monogeneric tribe Thaumatomyrmecini ( Thaumatomyrmex ) is newly synonymized under Ponerini (syn. nov.), and the diverse genus Pachycondyla is fragmented into 19 genera, largely along the lines of its junior synonyms: Bothroponera , Brachyponera (gen. rev.) , Ectomomyrmex (gen. rev.) , Euponera (gen. rev.) , Hagensia (gen. rev.) , Megaponera (gen. rev.) , Mesoponera (gen. rev.) , Neoponera (gen. rev.) , Ophthalmopone (gen. rev.) , Pachycondyla, Paltothyreus (gen. rev.) , Pseudoneoponera (gen. rev.) , Pseudoponera (gen. rev.), and 6 new genera: Austroponera (gen. nov.), Buniapone (gen. nov.), Fisheropone (gen. nov.), Mayaponera (gen. nov.), Parvaponera (gen. nov.) and Rasopone (gen. nov.). Some junior synonyms of Pachycondyla are transferred to junior synonym status under other genera: Wadeura as a junior synonym of Cryptopone (syn. nov.), and both Termitopone and Syntermitopone as junior synonyms of Neoponera (syn. nov.). A new genus, Iroponera (gen. nov.), based on the new species Iroponera odax (sp. nov.) , is described from Australia. Molecular and morphological justifications for these taxonomic changes are given alongside discussions of phylogenetic relationships. Keys to the world genera of Ponerinae are provided, and morphological diagnoses and species lists are given for each genus. Finally, the available information on ponerine ecology and behavior is reviewed and synthesized.

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The Higher Classification of the Ant Subfamily Ponerinae (Hymenoptera: Formicidae), with a Review of Ponerine Ecology and Behavior.

Optimal design of truss-structures using particle swarm optimization

The particle swarm optimization (PSO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the social behavior of birds flocking, is applied to problems of continuum structural topology design for the purpose of investigating optimal topologies and automatically creating innovative solutions. An overview of the PSO and binary PSO algorithms are first described. A discretized topology design representation and the method for mapping binary particle into this representation are then detailed. Subsequently, a modified binary PSO algorithm that adopts the concept of genotype-phenotype representation is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of structural topologies.

A binary particle swarm optimization for continuum structural topology optimization

The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described. A discretized topology design representation and the method for mapping ant's trail into this representation are then detailed. Subsequently, a modified ACO algorithm with elitist ants, niche strategy and memory of multiple colonies is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of multi-modal optimal design.

Structural topology optimization using ant colony optimization algorithm

An ant algorithm, consisting of the Ant System and API (after “apicalis” in Pachycondylaapicalis) algorithms, was proposed in this study to find optimal truss structures to achieve minimum weight objective under stress, deflection, and kinematic stability constraints. A two-stage approach was adopted in this study; first, the topology of the truss structure was optimized from a given ground structure employing the Ant System algorithm due to its discrete characteristic, and then the size and/or shape of member was optimized utilizing the API algorithm. The effectiveness of the proposed ant algorithm was evaluated through numerous different 2-D and 3-D truss-structure problems. The proposed algorithm was observed to find truss structures better than those reported in the literature. Moreover, multiple different truss topologies with almost equal overall weights can be found simultaneously.

Optimal design of truss structures using ant algorithm

This paper proposes a face recognition method using artificial immune networks based on principal component analysis (PCA). The PCA abstracts principal eigenvectors of the image in order to get best feature description, hence to reduce the number of inputs of immune networks. Henceforth these image data of reduced dimensions are input into immune network classifiers to be trained. Subsequently the antibodies of the immune networks are optimized using genetic algorithms. The performance of the present method was evaluated employing the AT&T Laboratories Cambridge database. The results show that this method gains higher recognition rate in contrast with most of the developed methods.

Chun-Yi Lin

Papers

Optimal design of truss-structures using particle swarm optimization

A binary particle swarm optimization for continuum structural topology optimization

Structural topology optimization using ant colony optimization algorithm

Optimal design of truss structures using ant algorithm

PCA based immune networks for human face recognition