Economic load dispatch solutions considering multiple fuels for thermal units and generation cost of wind turbines

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO

Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation

Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem \cite{BestounKamalFuzzy2017}. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system \cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems. We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.Comment: 21 page

Adaptive particle swarm optimization

An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity

Kajian terhadap ketahanan hentaman ke atas konkrit berbusa yang diperkuat dengan serat kelapa sawit

Konkrit berbusa merupakan sejenis konkrit ringan yang mempunyai kebolehkerjaan yang baik dan tidak memerlukan pengetaran untuk proses pemadatan. Umum mengenali konkrit berbusa sebagai bahan binaan yang mempunyai sifat kekuatan yang rendah dan lemah terutama apabila bahan binaan ini dikenakan tenaga hentaman yang tinggi. Namun begitu, konkrit berbusa merupakan bahan yang berpotensi untuk dijadikan sebagai bahan binaan yang berkonsepkan futuristik. Binaan futuristik adalah binaan yang bercirikan ringan, ekonomi, mudah dari segi kerja pembinaan dan yang paling penting adalah mesra alam. Dalam kajian ini, konkrit berbusa ditambah serat buangan pokok kelapa sawit untuk untuk meningkatkan sifat kekuatan atau rapuh. Serat kelapa sawit juga berfungsi mempertingkatkan ketahanan hentaman terutamanya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman. Kandungan peratusan serat kelapa sawit yang digunakan adalah 10%, 20% dan 30% dengan dua ketumpatan konkrit berbusa iaitu 1000kg/m3 dan 1400kg/m3 . Untuk menentukan nilai penyerapan tenaga hentaman dan nilai tenaga hentaman, ujikaji Indentasi dan ujikaji hentaman dilakukan ke atas sampel�sampel yang telah diawet selama 28 hari. Luas bawah graf tegasan-terikan yang diperolehi daripada ujikaji Indentasi merupakan nilai penyerapan tenaga hentaman bagi sampel konkrit berbusa. Untuk ujikaji hentaman, keputusan ujikaji dinilai berdasarkan nilai tenaga hentaman untuk meretakkan sampel yang diperolehi daripada mesin ujikaji dynatup. Secara keseluruhannya, hasil dapatan utama bagi kedua-dua ujikaji menunjukkan sampel yang mengandungi peratusan serat kelapa sawit sebanyak 20% mempunyai nilai penyerapan tenaga hentaman dan nilai tenaga hentaman yang tinggi. Serapan tenaga maksimum adalah sebanyak 4.517MJ/m3 untuk ketumpatan 1400kg/m3 . Ini menunjukkan ketumpatan 1400kg/m3 berupaya menyerap tenaga lebih baik berbanding ketumpatan 1000kg/m3 . Manakala untuk nilai tenaga hentaman maksimum adalah sebanyak 27.229J untuk ketumpatan 1400kg/m3 . Hasil dapatan tersebut menunjukkan ketumpatan 1400kg/m3 dengan peratusan serat sebanyak 20% berupaya mengalas tenaga hentaman yang lebih banyak sebelum sampel retak. Kesimpulannya, peningkatan ketumpatan konkrit berbusa dan pertambahan serat buangan kelapa sawit ke dalam konkrit berbusa dapat meningkatkan ciri ketahanan hentaman konkrit berbusa khususnya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman

An adaptive mutation operator for particle swarm optimization

Copyright @ 2008 MICParticle swarm optimization (PSO) is an effcient tool for optimization and search problems. However, it is easy to betrapped into local optima due to its in-formation sharing mechanism. Many research works have shown that mutation operators can help PSO prevent prema- ture convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compared for PSO. An adaptive mutation operator is designed. Experimental results show that these mutation operators can greatly enhance the performance of PSO. The adaptive mutation operator shows great advantages over non-adaptive mutation operators on a set of benchmark test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1