Shahrzad Saremi

Multi-objective grey wolf optimizer

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to design a multi-objective version of this algorithm. This paper proposes a Multi-Objective Grey Wolf Optimizer (MOGWO) in order to optimize problems with multiple objectives for the first time. A fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive is then employed to define the social hierarchy and simulate the hunting behavior of grey wolves in multi-objective search spaces. The proposed method is tested on 10 multi-objective benchmark problems and compared with two well-known meta-heuristics: Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D) and Multi-Objective Particle Swarm Optimization (MOPSO). The qualitative and quantitative results show that the proposed algorithm is able to provide very competitive results and outperforms other algorithms. Note that the source codes of MOGWO are publicly available at http://www.alimirjalili.com/GWO.html. A novel multi-objective algorithm called Multi-objective Grey Wolf Optimizer is proposed.MOGWO is benchmarked on 10 challenging multi-objective test problems.The quantitative results show the superior convergence and coverage of MOGWO.The coverage ability of MOGWO is confirmed by the qualitative results as well.

Biogeography-based optimisation with chaos

Abstract The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.

Grasshopper Optimisation Algorithm

The Grasshopper Optimisation Algorithm inspired by grasshopper swarms is proposed.The GOA algorithm is benchmarked on challenging test functions.The results on the unimodal functions show the superior exploitation of GOA.The exploration ability of GOA is confirmed by the results on multimodal and composite functions.The results on structural design problems confirm the performance of GOA in practice. This paper proposes an optimisation algorithm called Grasshopper Optimisation Algorithm (GOA) and applies it to challenging problems in structural optimisation. The proposed algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems. The GOA algorithm is first benchmarked on a set of test problems including CEC2005 to test and verify its performance qualitatively and quantitatively. It is then employed to find the optimal shape for a 52-bar truss, 3-bar truss, and cantilever beam to demonstrate its applicability. The results show that the proposed algorithm is able to provide superior results compared to well-known and recent algorithms in the literature. The results of the real applications also prove the merits of GOA in solving real problems with unknown search spaces.

Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

A novel optimization algorithm called Salp Swarm Optimizer (SSA) is proposed.Multi-objective Salp Swarm Algorithm (MSSA) is proposed to solve multi-objective problems.Both algorithms are tested on several mathematical optimization functions.Two challenging engineering design problems are solved: airfoil design and marine propeller design.The qualitative and quantitative results prove the efficiency of SSA and MSSA. This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.

Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems

This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a roulette wheel mechanism based on the coverage of solutions as antlions to guide ants towards promising regions of multi-objective search spaces. To prove the effectiveness of the algorithm proposed, a set of standard unconstrained and constrained test functions is employed. Also, the algorithm is applied to a variety of multi-objective engineering design problems: cantilever beam design, brushless dc wheel motor design, disk brake design, 4-bar truss design, safety isolating transformer design, speed reduced design, and welded beam deign. The results are verified by comparing MOALO against NSGA-II and MOPSO. The results of the proposed algorithm on the test functions show that this algorithm benefits from high convergence and coverage. The results of the algorithm on the engineering design problems demonstrate its applicability is solving challenging real-world problems as well.

How important is a transfer function in discrete heuristic algorithms

Abstract Transfer functions are considered the simplest and cheapest operators in designing discrete heuristic algorithms. The main advantage of such operators is the maintenance of the structure and other continuous operators of a continuous algorithm. However, a transfer function may show different behaviour in various heuristic algorithms. This paper investigates the behaviour and importance of transfer functions in improving performance of heuristic algorithms. As case studies, two algorithms with different mechanisms of optimisation were chosen: Gravitational Search Algorithm and Particle Swarm Optimisation. Eight transfer functions were integrated in these two algorithms and compared on a set of test functions. The results show that transfer functions may show diverse behaviours and have different impacts on the performance of algorithms, which should be considered when designing a discrete algorithm. The results also demonstrate the significant role of the transfer function in terms of improved exploration and exploitation of a heuristic algorithm.

Integrating Chaos to Biogeography-Based Optimization Algorithm

This work integrates chaotic maps into the recently proposed heuristic algorithm called Biogeography-Based Optimization (BBO) algorithm. The effects of 3 different chaotic maps such as Circle, Sine, and Sinusoidal on improving the performance of BBO are investigated in terms of local optima avoidance and convergence speed. The algorithms are benchmarked on four thirty-dimensional test function such as Sphere, Schwefel, Rasterigin, and Griewank. The results prove that chaotic maps (especially Sine map) are able to improve the performance of BBO.

Grasshopper optimization algorithm for multi-objective optimization problems

This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution.

Optimization of problems with multiple objectives using the multi-verse optimization algorithm

This work proposes the multi-objective version of the recently proposed Multi-Verse Optimizer (MVO) called Multi-Objective Multi-Verse Optimizer (MOMVO). The same concepts of MVO are used for converging towards the best solutions in a multi-objective search space. For maintaining and improving the coverage of Pareto optimal solutions obtained, however, an archive with an updating mechanism is employed. To test the performance of MOMVO, 80 case studies are employed including 49 unconstrained multi-objective test functions, 10 constrained multi-objective test functions, and 21 engineering design multi-objective problems. The results are compared quantitatively and qualitatively with other algorithms using a variety of performance indicators, which show the merits of this new MOMVO algorithm in solving a wide range of problems with different characteristics.

Enhanced multi-objective particle swarm optimisation for estimating hand postures

Abstract Multi-objective problems with conflicting objectives cannot be effectively solved by aggregation-based methods. The answer to such problems is a Pareto optimal solution set. Due to the difficulty of solving multi-objective problems using multi-objective algorithms and the lack of enough expertise, researchers in different fields tend to aggregative objectives and use single-objective algorithms. This work is a seminal attempt to propose the use of multi-objective algorithms in the field of hand posture estimation. Hand posture estimation is a key step in hand gesture recognition, which is a part of an overall attempt to make human-computer interaction more like human face-to-face communication. Hand posture estimation is first formulated as a bi-objective problem. A modified version of Multi-Objective Particle Swarm Optimisation (MOPSO) is then proposed to approximate the Pareto optimal font of 50 different postures. The main motivation of integrating a new operator (called Evolutionary Population Dynamics — EPD) in MOPSO is due to the nature of hand posture estimation problems in which parameters should not be tuned in a same manner since they show varied impacts on the objectives. EPD allows randomising different parameters in a solution and provides different exploratory behaviours for the parameters of an optimisation algorithm rather than each individual solution. The MOPSO algorithm is equipped with a mechanism to randomly re-initialise poor particles around the optimal solutions in the archive. The improved MOPSO is tested on ZDT and CEC2009 test functions and compared with the standard MOPSO, NSGA-II, and MOEA/D. The results show that the proposed MOPSO (MOPSO+EPD) significantly outperforms MOPSO on the majority of test functions in terms of both convergence and coverage. MOPSO+EPD also approximates well-distributed Pareto optimal fronts for most of the postures considered in this work. The post analysis of the results is conducted to understand the relationship between the parameters and objectives of this problem (design principals) for the first time in the literature as well.